Virginia Web Scraping

Virginia Data Scraping, Web Scraping Tennessee, Data Extraction Tennessee, Scraping Web Data, Website Data Scraping, Email Scraping Tennessee, Email Database, Data Scraping Services, Scraping Contact Information, Data Scrubbing

Wednesday, 31 December 2014

Hand Scraped Flooring: Points to Keep in Mind

The demand for hand-scraped flooring is growing. Yet, this type of flooring, in terms of appearance, isn't like any other. If you are one of the many considering it for your home, what points do you need to keep in mind as you look for the right type of hand-scraped hardwood?

First, nearly all species – domestic and exotic – are available as this distressed variety. Species from white oak to Brazilian cherry are all available with this distressed and rustic look. And, any floor of a building can have hand-scraped flooring, as both solid and engineered types are distressed. As you look at different types of hand-scraped flooring, think about where you will be installing it into your home, and plan accordingly with the right type of solid or engineered hardwood.

What's most notable about hand-scraped hardwood is its creation. All planks are distressed by hand, and as a result, no two appear similar. Multiple methods are used for distressing hardwood, including the following techniques for aging, scraping, or finishing.

Aged hardwood goes by one of two names: Time Worn Aged or Antique. Both are similar, but a lower grade is used for Antique flooring. In addition to being aged, the hardwood's distressed appearance is accented further through darker staining, highlighting the grain, or contouring.

Scraping techniques alter the texture of the hardwood, making an otherwise smooth surface rough. Wire Brushed is a term used to indicate hand-scraped flooring with removed sapwood and accented grain. Hand-sculpted, on the other hand, still has texture but is smoother than other varieties. Hardwood that is Hand Hewn and Rough Sawn has the roughest texture for hand-scraped flooring, with even saw marks visible.

Flooring that uses finish to give hardwood an aged texture is usually sold as French Bleed. Such hand-scraped flooring has deeper beveled edges, and the joints of the floor are highlighted with darker stain. Also a somewhat superficial type of hand-scraped flooring is pegged. Considered to be decorative only, pegged flooring must not be fastened directly onto a subfloor.

If you want an even less uniform appearance for your floor, consider having it custom distressed. In this case, after the unfinished hardwood is installed, a professional comes in to alter it through beating with chains, pickeling, fastening with antique nails, or bleaching. After, a finish is applied.

Also as you look at hand-scraped hardwood, think about your flooring long term. Will you want a distressed appearance a decade or more down the line? If not, plan ahead by going with flooring that can be sanded down: solid hardwood or an engineered variety with a thicker wear layer.

If, on the other hand, you plan to keep the hand-scraped flooring, think about how you will refinish it years down the line. Ideally, to keep up the distressed look without diminishing it through sanding, you will need a floor abrader to remove only the finish, or be prepared to have a professional refinish your floors.


Monday, 29 December 2014

Web Data Scraping Services Have Various Method Of Business

Magnetic or optical data removal or Data Scraping Services is a term that refers to the elimination of digital storage media. Data Scraping Services of the method varies, depending on medium and method used in the process.

Similarly, patents, models, business strategies and other confidential business information, including sensitive data, can be easily accessed by others if the data is not deleted.As I said in the beginning, Data Scraping Services methods vary depending on the storage medium. For each storage medium, there are a variety of Data Scraping Services techniques.

Optical media such as  that can be destroyed by the plastic granulating. This method does not extract information, but makes recovery almost impossible. However, removal of thin film that coats the top of the disk, scraping, sanding by hand or destroy physical data. In contrast, using the microwave, a less traditional technologies, stable and disk storage layer of the thin film is very effective for the most common cause sparks to load.

Typical modern magnetic media and hard drives, tape backup units of such media is possible, but in the face of such devices requires considerable financial investment in the plant. Acids, in particular, nitric acid, 50% concentration in the iron oxide layer to react with violence, it will be completely destroyed within a few minute. In some cases it may be a storage alternative for incineration. However, this may inadvertently expose caseinogens operator and may be restricted in certain countries.

Data Scraping Services, on the other hand, is defined by Wikipedia as "an automatic search for large stores of data for patterns of practice." In other words, you already know, and you learn things about it useful analysis.

Data Scraping Services is often accompanied by a lot of complex algorithms based on statistical methods. How do you see the data in the first place - is not. Data Scraping Services analysis, you only care about what is already there in many cases, a single-pass binary wipe (to write random zeroes and ones riding) will permanently deletes all data from the storage device to remove.

use of materials recovery.
It is for this reason that the technology has been left until last.
Data Scraping Services, screen scraping is not.
This is a great simplification, so I will work a bit.

Fast-forwarding to the web world today, screen scraping is the information relates to websites. This means that computer programs "crawl" or can "spider" through web sites, data retrieval. people, We deserved pages, text data Scraping Services, automated data collection, data extraction and web site even bloody website if we have a problem it presents some.

Data Scraping Services, on the other hand, is defined by Wikipedia as "an automatic search for large stores of data for patterns of practice." In other words, you already know, and you learn things about it useful analysis. Data Scraping Services is often accompanied by a lot of complex algorithms based on statistical methods. How do you see the data in the first place - is not. Data Scraping Services analysis, you only care about what is already there.


Saturday, 27 December 2014

Scraping By

In his classic 1976 Chesapeake portrait, Beautiful Swimmers, William Warner described the scrape boat as "a workboat unlike any other I had ever seen on the Bay." Seeming half as wide as it was long, he said, it looked like a "a miniature battleship." There's a reason for that, of course. It's a classic case of form following function; the boat evolved for one purpose, to ply the Bay's grassy shallows for shedding blue crabs.

Said to "float on a heavy dew," scrape boats run from 26 to 30 feet long and 9 to 10 feet wide. The hull is a shallow-V deadrise that quickly flattens toward the stern, enabling the boat to pull its twin scrapes—rectangular steel frames, each with a trailing mesh bag—in knee-deep waters. The broad beam might sound ungainly, but the hull tapers toward the stern—betraying its sailboat origins. And it has a graceful sheer, flowing from a bow height of a few feet to little more than a foot above the water amidships.

And you want a low freeboard when you spend the whole day hoisting aboard scrapes, which weigh 50 pounds apiece, not including the load of sea grass and crabs that come in too. Low sides or not, there's a higher than average inci-dence of back problems among scrape boat crabbers. They spend long days bending in precisely the position back doctors say puts undue pressure on the lower back as they sort through rolls of grasses to pluck out the peelers and softies. And that alone may be why crab potting is now the far more common way of catching soft crabs.

Some people think that's good, assuming that dragging a scrape across the Bay's beleaguered grass flats must be destructive. But the smooth bar of the scrape, unlike a toothed dredge, doesn't uproot grasses. In fact, where scraping is traditional, the grass beds seem relatively resilient. I've often thought if Maryland and Virginia had stuck with scraping as the major legal way to soft-crab, overfishing might not have become a problem. Pots can be deployed everywhere and by the thousands, whereas scraping is limited to grass beds and to ground covered at three miles per hour; and even the sturdiest waterman can only pull two of them by hand. But peeler pots seem here to stay, and other soft crabbers have taken to using a single, large scrape operated from larger workboats by hydraulic power.

The bottom line is that these lovely, superbly functional expressions of Chesapeake crabbing culture now number only in the dozens, if you count working, wooden models. There are some fiberglass scrape boat hulls in service, and a Carolina skiff or two has been adapted for the task. They are functional, but have little art to them.

It is probably a sign of how fast scrape boats are going that the Smithsonian Institution recently took the lines off Darlene, a scraper worked by Morris Marsh of Smith Island, for its archives. You can see photos of scrape boats, and learn more about the 140-year old history of scraping, from Paula Johnson's fine book, The Workboats of Smith Island. Mr. Marsh, still going strong in his late 60s, is the scraper who took Warner out nearly 40 years ago when he was researching Beautiful Swimmers.

Indeed, scraping seems to win over those who master it. Marsh's father-in-law, Ed Harrison, scraped for almost 70 years, nearly wearing through the cross-planked bottom of his boat—from the inside—with decades of walking the planks, tending his scrapes. And an islander who scrapes with Marsh today, David Laird, says he is 71—one year younger than Scotty Boy, the scrape boat he took over from his dad in 1958. "I wouldn't even know how to crab in another boat," Laird says.

Soft crabs may well be caught—or farmed—a century from now on the Chesapeake; but no one will devise a way to take them so intimately and beautifully from the shallowest marsh edges and tiniest crevices in the shore as the scrapers do.


Friday, 26 December 2014

Choose Mining Wear Parts Wisely

It is important to choose a reputable supplier of mining wear parts; one that has been acknowledged as a leader in mining expertise. You will want to research and seek out a company that specializes in the engineering, manufacturing, procurement and design of mining wear parts and who has access to a multitude of patterns and templates to choose from.

It is vital to find a company that invites you to put them to the test; a company that is committed to selling more than just a product, standing behind the parts that they design and manufacture with an unprecedented industry guarantee. Some companies are so confident in their products that each wear part is stamped with their logo, identifying it as a superior product.

You will also want to find a company that takes pride in establishing strong customer relationships and who employs people who are as equally committed to providing outstanding service with customer satisfaction a priority. Your research will help you find a mining wear parts company that guarantees that if they do not have the part available, that they will find it for you or are capable of custom designing products to your exact specifications.

If you stop to consider the ramifications of an equipment malfunction or breakdown on production quotas, the significance of reliable parts becomes readily apparent. The impact can be far reaching if it halts production while the necessary repairs are completed. The ugly reality is that downtime incurs financial losses.

While the cost of aftermarket replacement mining wear parts is one factor, the installation of the part is equally as important. It is vital that aftermarket parts are built to a rugged standard to endure the rigorous industrial demands placed on them. Mining wear parts are routinely subjected to high stress abrasion and impact. The fabricated parts need to have the structural strength to be wear resistant with extended usage. Hardened manganese is the preferred material of choice to impart added strength and avoid premature breakage and replacement. Using inferior quality parts may result in the necessity of replacing them prematurely if they do not withstand the wear and tear that they are subjected to daily. While a few dollars may be saved initially by purchasing inferior mining wear parts, production costs can dramatically increase if frequent breakdowns occur and manpower hours are wasted in the field. Efficient use of manpower is an important budget consideration. Reliability is an absolute necessity w
hen you have production deadlines to meet and operations can quickly grind to a standstill when production is halted.

Quality assurance management monitors the consistency of the parts, demanding that they are machined within precise measurements. In addition, they focus on striving to improve the quality of parts as new technology becomes available. Using precision made, high quality wear parts can make your business more competitive, giving you an advantage and improving your bottom line.


Tuesday, 23 December 2014

Scraping table from any web page with R or CloudStat

Scraping table from any web page with R or CloudStat:

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from


airline = ‘’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)


> library(XML)

Warning message:

package "XML" was built under R version 2.14.1

> airline = ""

> airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

> airline.table

                     Base-line 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10

1          Southwest        78 76 76 76 74 72 70 70 74 75 73 74 74 76 79 81 79
2         All Others        NM 70 74 70 62 67 63 64 72 74 73 74 74 75 75 77 75
3           Airlines        72 69 69 67 65 63 63 61 66 67 66 66 65 63 62 64 66
4        Continental        67 64 66 64 66 64 62 67 68 68 67 70 67 69 62 68 71
5           American        70 71 71 62 67 64 63 62 63 67 66 64 62 60 62 60 63
6             United        71 67 70 68 65 62 62 59 64 63 64 61 63 56 56 56 60
7         US Airways        72 67 66 68 65 61 62 60 63 64 62 57 62 61 54 59 62
8              Delta        77 72 67 69 65 68 66 61 66 67 67 65 64 59 60 64 62
9 Northwest Airlines        69 71 67 64 63 53 62 56 65 64 64 64 61 61 57 57 61
  11 PreviousYear%Change FirstYear%Change
1 81                 2.5              3.8
3 65                -1.5             -9.7
4 64                -9.9             -4.5
5 63                 0.0            -10.0
7 61                -1.6            -15.3
8 56                -9.7            -27.3
9  #                 N/A              N/A


B. Scraping World Top Chess players (Men) table from


chess = ‘’

chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)


> chess = ""

> chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

> chess.table

     Rank                       Name Title Country Rating Games B-Year

1      1           Carlsen, Magnus    g    NOR  2835   17  1990
2      2            Aronian, Levon    g    ARM  2805   25  1982
3      3         Kramnik, Vladimir    g    RUS  2801   17  1975
4      4        Anand, Viswanathan    g    IND  2799   17  1969
5      5         Radjabov, Teimour    g    AZE  2773    9  1987
6      6          Topalov, Veselin    g    BUL  2770    9  1975
7      7          Karjakin, Sergey    g    RUS  2769   16  1990
8      8         Ivanchuk, Vassily    g    UKR  2766   16  1969
9      9     Morozevich, Alexander    g    RUS  2763    6  1977
10    10           Gashimov, Vugar    g    AZE  2761    9  1986
11    11       Grischuk, Alexander    g    RUS  2761    8  1983
12    12          Nakamura, Hikaru    g    USA  2759   17  1987
13    13            Svidler, Peter    g    RUS  2749   17  1976
14    14    Mamedyarov, Shakhriyar    g    AZE  2747    9  1985
15    15       Tomashevsky, Evgeny    g    RUS  2740    0  1987
16    16            Gelfand, Boris    g    ISR  2739    9  1968
17    17          Caruana, Fabiano    g    ITA  2736   19  1992
18    18       Nepomniachtchi, Ian    g    RUS  2735   16  1990
19    19                 Wang, Hao    g    CHN  2733    6  1989
20    20              Kamsky, Gata    g    USA  2732    0  1974
21    21  Dominguez Perez, Leinier    g    CUB  2730    6  1983
22    22         Jakovenko, Dmitry    g    RUS  2729    0  1983
23    23        Ponomariov, Ruslan    g    UKR  2727   13  1983
24    24          Vitiugov, Nikita    g    RUS  2726    1  1987
25    25            Adams, Michael    g    ENG  2724   17  1971
26    26               Leko, Peter    g    HUN  2720    9  1979
27    27            Almasi, Zoltan    g    HUN  2717    8  1976
28    28               Giri, Anish    g    NED  2714   15  1994
29    29            Le, Quang Liem    g    VIE  2714    0  1991
30    30             Navara, David    g    CZE  2712    8  1985
31    31            Shirov, Alexei    g    LAT  2710   13  1972
32    32             Polgar, Judit    g    HUN  2710    0  1976
33    33     Riazantsev, Alexander    g    RUS  2710    0  1985
34    34       Wojtaszek, Radoslaw    g    POL  2706    8  1987
35    35      Moiseenko, Alexander    g    UKR  2706    7  1980
36    36   Vallejo Pons, Francisco    g    ESP  2705   15  1982
37    37        Malakhov, Vladimir    g    RUS  2705    0  1980
38    38            Jobava, Baadur    g    GEO  2704   23  1983
39    39           Bacrot, Etienne    g    FRA  2704   14  1983
40    40          Laznicka, Viktor    g    CZE  2704    8  1988
41    41            Sutovsky, Emil    g    ISR  2703    8  1977
42    42        Naiditsch, Arkadij    g    GER  2702   14  1985
43    43         Movsesian, Sergei    g    ARM  2700    9  1978
44    44       Sasikiran, Krishnan    g    IND  2700    9  1981
45    45   Vachier-Lagrave, Maxime    g    FRA  2699   13  1990
46    46            Dreev, Aleksey    g    RUS  2698    6  1969
47    47           Efimenko, Zahar    g    UKR  2695    8  1985
48    48         Volokitin, Andrei    g    UKR  2695    0  1986
49    49                 Wang, Yue    g    CHN  2694    6  1987
50    50        Fressinet, Laurent    g    FRA  2693   17  1981
51    51                Li, Chao b    g    CHN  2693    6  1989
52    52            Grachev, Boris    g    RUS  2693    0  1986
53    53      Nielsen, Peter Heine    g    DEN  2693    0  1973
54    54            Van Wely, Loek    g    NED  2692   13  1972
55    55    Bruzon Batista, Lazaro    g    CUB  2691   19  1982
56    56           McShane, Luke J    g    ENG  2691    8  1984
57    57            Eljanov, Pavel    g    UKR  2690   10  1983
58    58      Kasimdzhanov, Rustam    g    UZB  2689   14  1979
59    59         Inarkiev, Ernesto    g    RUS  2689    6  1985
60    60         Zvjaginsev, Vadim    g    RUS  2688    8  1976
61    61         Andreikin, Dmitry    g    RUS  2688    0  1990
62    62    Areshchenko, Alexander    g    UKR  2688    0  1986
63    63         Rublevsky, Sergei    g    RUS  2686    0  1974
64    64         Akopian, Vladimir    g    ARM  2685    8  1971
65    65          Potkin, Vladimir    g    RUS  2684    0  1982
66    66       Sargissian, Gabriel    g    ARM  2683   15  1983
67    67            Berkes, Ferenc    g    HUN  2682   16  1985
68    68           Bologan, Viktor    g    MDA  2680   15  1971
69    69          Bauer, Christian    g    FRA  2679   24  1977
70    70          Tiviakov, Sergei    g    NED  2677   22  1973
71    71            Short, Nigel D    g    ENG  2677   15  1965
72    72        Motylev, Alexander    g    RUS  2677    6  1979
73    73         Gharamian, Tigran    g    FRA  2676    0  1984
74    74          Kobalia, Mikhail    g    RUS  2673    0  1978
75    75              Meier, Georg    g    GER  2671    9  1987
76    76       Onischuk, Alexander    g    USA  2670   13  1975
77    77              Bu, Xiangzhi    g    CHN  2670    6  1985
78    78          Alekseev, Evgeny    g    RUS  2670    0  1985
79    79            Azarov, Sergei    g    BLR  2667    0  1983
80    80        Kryvoruchko, Yuriy    g    UKR  2666    0  1986
81    81             Balogh, Csaba    g    HUN  2665    8  1987
82    82           Harikrishna, P.    g    IND  2665    6  1986
83    83       Khismatullin, Denis    g    RUS  2664    8  1984
84    84   Nguyen, Ngoc Truong Son    g    VIE  2662    6  1990
85    85           Fridman, Daniel    g    GER  2660   11  1976
86    86              Smirin, Ilia    g    ISR  2660    7  1968
87    87               Ding, Liren    g    CHN  2660    6  1992
88    88         Sadler, Matthew D    g    ENG  2660    3  1974
89    89            Korobov, Anton    g    UKR  2660    0  1985
90    90          Cheparinov, Ivan    g    BUL  2659   18  1986
91    91          Timofeev, Artyom    g    RUS  2659    0  1985
92    92           Georgiev, Kiril    g    BUL  2658   17  1965
93    93           Bartel, Mateusz    g    POL  2658    9  1985
94    94          Zhigalko, Sergei    g    BLR  2658    8  1989
95    95         Feller, Sebastien    g    FRA  2658    0  1991
96    96            Ragger, Markus    g    AUT  2655   17  1988
97    97         Jones, Gawain C B    g    ENG  2653   27  1987
98    98                So, Wesley    g    PHI  2653    5  1993
99    99              Milov, Vadim    g    SUI  2653    0  1972
100  100           Gupta, Abhijeet    g    IND  2652    9  1989
101  101            Postny, Evgeny    g    ISR  2652    8  1981
102  102             Roiz, Michael    g    ISR  2652    6  1983
103  103           Gyimesi, Zoltan    g    HUN  2652    4  1977
104  104          Nikolic, Predrag    g    BIH  2652    2  1960


Done. You had successfully scraping data from any web page with R or CloudStat.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!


Sunday, 21 December 2014

Extracting Wisdom Teeth Tips

It is believed that due to evolution, our jaws are now smaller than our ancient ancestors'. For this reason, our mouths often do not have adequate room to accommodate the third molars, making them basically useless and in some cases detrimental. Even if they are not impacted, wisdom teeth may be hard to clean, and therefore require removal to reduce the probability of caries and infection.

As part of your routine dental visits, your dentist will likely take X-rays to monitor the development of your third molars. Your dentist will likely recommend removing them as soon as possible to avoid any complications. The extraction of wisdom teeth can sometimes be a costly and daunting procedure; for these reasons many patients delay having them extracted. However, if the impacted teeth become infected, it is important to see your dental professional at once. Symptoms of infection due to impacted wisdom teeth include;

•    Pain in the gums and surrounding areas
•    Red or inflamed gums
•    Tender or bleeding gums
•    Inflammation around the face and jaw
•    Bad breath (halitosis)
•    Frequent headaches

If a single molar needs to be extracted, local anesthetic will be used. In the case where several or all the teeth need extraction, the patient will usually be "put under" using a general anesthetic. If you have an infection or medical complications that put you at a higher than normal risk, the surgery may be performed at a hospital. Extraction of the wisdom teeth is a day surgery, and patients are usually able to return to normal activities in a day or so. You may be prescribed antibiotics prior to the surgery, and you will likely be asked not to eat or drink the night before the surgery.

During the surgery, your dentist makes an incision in the gum tissue covering the tooth. Once the tooth is exposed, the dentist may cut the tooth into smaller pieces to make extraction easier. After the extraction you will be given stitches to mend the gum tissue. You may need to return a few days later to have the stitches removed. You will be monitored after the surgery to ensure that you are not bleeding excessively.

The best time for extraction is when the patient is in their late teens to avoid unnecessary complications. Wisdom teeth extractions performed later in life are still beneficial, but the removal may be more difficult and healing may take longer. Therefore it is wise to have a conversation with your dentist regarding your wisdom teeth as early as possible.

Most people will experience the emergence of their wisdom teeth at some point in their life, and extraction is sometimes necessary as a preventative measure or to fix an actual problem or to prevent problem. It is best to deal with any problems regarding your wisdom teeth as soon as possible to avoid unnecessary difficulties.


Wednesday, 17 December 2014

Importance of Data Mining Services in Business

Data mining is used in re-establishment of hidden information of the data of the algorithms. It helps to extract the useful information starting from the data, which can be useful to make practical interpretations for the decision making.

It can be technically defined as automated extraction of hidden information of great databases for the predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making. Although data mining is a relatively new term, the technology is not. It is thus also known as Knowledge discovery in databases since it grip searching for implied information in large databases.

It is primarily used today by companies with a strong customer focus - retail, financial, communication and marketing organizations. It is having lot of importance because of its huge applicability. It is being used increasingly in business applications for understanding and then predicting valuable data, like consumer buying actions and buying tendency, profiles of customers, industry analysis, etc. It is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, e-commerce, customer relationship management and financial services.

However, the use of some advanced technologies makes it a decision making tool as well. It is used in market research, industry research and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, scientific tests, genetics, financial services and utilities.

Data mining consists of major elements:

•    Extract and load operation data onto the data store system.
•    Store and manage the data in a multidimensional database system.
•    Provide data access to business analysts and information technology professionals.
•    Analyze the data by application software.
•    Present the data in a useful format, such as a graph or table.

The use of data mining in business makes the data more related in application. There are several kinds of data mining: text mining, web mining, relational databases, graphic data mining, audio mining and video mining, which are all used in business intelligence applications. Data mining software is used to analyze consumer data and trends in banking as well as many other industries.


Tuesday, 16 December 2014

Autoscraping casts a wider net

We have recently started letting more users into the private beta for our Autoscraping service. We’re receiving a lot of applications following the shutdown of Needlebase and we’re increasing our capacity to accommodate these users.

Natalia made a screencast to help our new users get started:

It’s also a great introduction to what this service can do.

We released slybot as an open source integration of the scrapely extraction library and the scrapy framework. This is the core technology behind the autoscraping service and we will make it easy to export autoscraping spiders from Scrapinghub  and run them completely with slybot – allowing our users to have the flexibility and freedom provided by open source.


Monday, 15 December 2014

Local ScraperWiki Library

It quite annoyed me that you can only use the scraperwiki library on a ScraperWiki instance; most of it could work fine elsewhere. So I’ve pulled it out (well, for Python at least) so you can use it offline.

How to use
pip install scraperwiki_local
A dump truck dumping its payload

You can then import scraperwiki in scripts run on your local computer. The scraperwiki.sqlite component is powered by DumpTruck, which you can optionally install independently of scraperwiki_local.

pip install dumptruck

DumpTruck works a bit differently from (and better than) the hosted ScraperWiki library, but the change shouldn’t break much existing code. To give you an idea of the ways they differ, here are two examples:

Complex cell values
What happens if you do this?
import scraperwiki
shopping_list = ['carrots', 'orange juice', 'chainsaw'][], {'shopping_list': shopping_list})
On a ScraperWiki server, shopping_list is converted to its unicode representation, which looks like this:
[u'carrots', u'orange juice', u'chainsaw']
In the local version, it is encoded to JSON, so it looks like this:
["carrots","orange juice","chainsaw"]

And if it can’t be encoded to JSON, you get an error. And when you retrieve it, it comes back as a list rather than as a string.

Case-insensitive column names
SQL is less sensitive to case than Python. The following code works fine in both versions of the library.

In [1]: shopping_list = ['carrots', 'orange juice', 'chainsaw']
In [2]:[], {'shopping_list': shopping_list})
In [3]:[], {'sHOpPiNg_liST': shopping_list})
In [4]:'* from swdata')

Out[4]: [{u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}, {u'shopping_list': [u'carrots', u'orange juice', u'chainsaw']}]

Note that the key in the returned data is ‘shopping_list’ and not ‘sHOpPiNg_liST’; the database uses the first one that was sent. Now let’s retrieve the individual cell values.

In [5]: data ='* from swdata')
In [6]: print([row['shopping_list'] for row in data])
Out[6]: [[u'carrots', u'orange juice', u'chainsaw'], [u'carrots', u'orange juice', u'chainsaw']]

The code above works in both versions of the library, but the code below only works in the local version; it raises a KeyError on the hosted version.

In [7]: print(data[0]['Shopping_List'])
Out[7]: [u'carrots', u'orange juice', u'chainsaw']

Here’s why. In the hosted version, returns a list of ordinary dictionaries. In the local version, returns a list of special dictionaries that have case-insensitive keys.

Develop locally

Here’s a start at developing ScraperWiki scripts locally, with whatever coding environment you are used to. For a lot of things, the local library will do the same thing as the hosted. For another lot of things, there will be differences and the differences won’t matter.

If you want to develop locally (just Python for now), you can use the local library and then move your script to a ScraperWiki script when you’ve finished developing it (perhaps using Thom Neale’s ScraperWiki scraper). Or you could just run it somewhere else, like your own computer or web server. Enjoy!


Friday, 12 December 2014

Seven tools for web scraping – To use for data journalism & creating insightful content

I’ve been creating a lot of (data driven) creative content lately and one of the things I like to do is gathering as much data as I can from public sources. I even have some cases it is costing to much time to create and run database queries and my personal build PHP scraper is faster so I just wanted to share some tools that could be helpful. Just a short disclaimer: use these tools on your own risk! Scraping websites could generate high numbers of pageviews and with that, using bandwidth from the website you are scraping.

1. Scraper (Chrome plugin)

    Scraper is a simple data mining extension for Google Chrome™ that is useful for online research when you need to quickly analyze data in spreadsheet form.

You can select a specific data point, a price, a rating etc and then use your browser menu: click Scrape Similar and you will get multiple options to export or copy your data to Excel or Google Docs. This plugin is really basic but does the job it is build for: fast and easy screen scraping.

2. Simple PHP Scraper

PHP has a DOMXpath function. I’m not going to explain how this function works, but with the script below you can easily scrape a list of URLs. Since it is PHP, use a cronjob to hourly, daily or weekly scrape the desired data. If you are not used to creating Xpath references, use the Scraper for Chrome plugin by selecting the data point and see the Xpath reference directly.


– Click here to download the example script.

3. Kimono Labs

Kimono has two easy ways to scrape specific URLs: just paste the URL into their website or use their bookmark. Once you have pointed out the data you need, you can set how often and when you want the data to be collected. The data is saved in their database. I like the facts that their learning curve is not that steep and it doesn’t look like you need a PHD in engineering to use their software. The disadvantage of this tool is the fact you can’t upload multiple URLs at once.

4. is a browser based web scraping tool. By following their easy step-by-step plan you select the data you want to scrape and the tool does the rest. It is a more sophisticated tool compared to Kimono. I like it because of the fact it shows a clear overview of all the scrapers you have active and you can scrape multiple URLs at once.

5. Outwit Hub

I will start with the two biggest differences compared to the previous tool: it is a softwarepackage to use on your PC or laptop and to use its full potential it will cost you 75 USD. The free version can only scrape 100 rows of data. What I do like is the number of preprogrammed options to scrape which makes it easy to start and learn about web scraping.

6. ScraperWiki

This tool is really for people wanting to scrape on a massive scale. You can code your own scrapers (in PHP, Ruby & Python) and pricing is really cheap looking to what you can get: 29USD / month for 100 datasets. You are completely free in using libraries and timers. And if your programming skills are not good enough, they can help you out (paid service though). Compared to other tools, this is the most advanced tool that offers the basics of web scraping.

This tool made it possible to finally scrape all the data inside Google Webmaster Tools since it can deal with JavaScript and AJAX interfaces. Read my extensive review on this page: Scraping Webmaster Tools with FMiner!

But on the end, building your individual project scrapers will always be more effective than using predefined scrapers. Am I missing any tools in this sum up of tools?


Wednesday, 10 December 2014

Multiple Listing Service Gets Favorable Appellate Ruling in Scraping Lawsuit

This is a follow-up to our massive post on anti-scraping lawsuits in the real estate industry from New Year’s Eve 2012 (Note: the portion on MRIS is about halfway through the post, labeled “Same Writ, Different Plaintiff”).

AHRN is a California real estate broker that owns and operates The site gets its data in part by scraping from MLS databases–in this case, MRIS. As part of the scraping, however, AHRN had collected and displayed copyrighted photographs among the bits and pieces of general textual information about the properties. MRIS sent a cease and desist letter to AHRN, and filed suit alleging various copyright claims after the parties failed to agree on a license to use the photographs. Ultimately, a district court in Maryland granted a motion made by MRIS for a preliminary injunction.

When we last left off, the district court had revised its preliminary injunction order to enjoin only AHRN’s use of MRIS’s photographs–not the compilation itself or any textual elements that may be considered a part of it. Since then, AHRN appealed the injunction. On July 18th, the Fourth Circuit Court of Appeals affirmed.


shutterstock_108008486.jpgAHRN argued that MRIS failed to show a likelihood of success on its copyright infringement claim because MRIS: (1) failed to register its copyright in the individual photographs when it registered the database, and (2) did not have a copyright interest in the photographs because the subscribers’ electronic agreement to MRIS’s terms of use failed to transfer those rights.

 MRIS Did Not Fail to Register Its Interest in the Photographs

This first question revolved around the scope of MRIS’s registrations. AHRN argued that MRIS’s collective work registrations did not cover the individual photographs because MRIS did not identify the names of the authors and titles of those works. MRIS argued that 17 U.S.C. §409 did not require any such identification when applied to collective works, and that its general description of the pre-existing photographs’ inclusion sufficed.

The court began its discussion by noting the “ambiguous” nature of §409’s language and its varying judicial interpretations. Some courts have barred infringement suits because the collective work registrant failed to list the authors, while others have allowed infringement suits where the registrant owns the rights to the component works as well as the collective work.

In this case, the court agreed with MRIS and found that the latter approach was more consistent with the relevant statutes and regulations:

    Adding impediments to automated database authors’ attempts to register their own component works conflicts with the general purpose of Section 409 to encourage prompt registration . . . and thwarts the specific goal embodied in Section 408 of easing the burden on group registrations[.]

As part of its decision, the court looked favorably upon the 3Taps case, in which Craigslist sued 3Taps and Padmapper for scraping and repackaging its online classified ads. In that case, the court reasoned that it would be “inefficient” to require registrants to list each author of an extremely large number of component works to which the registrant already had obtained an exclusive license.

Having found that MRIS’s general description satisfied § 409’s pre-suit registration requirement, the court moved on to the merits of MRIS’s infringement claim–more specifically, the question of whether MRIS’s Terms of Use actually transferred a copyright interest to its subscribers’ photographs.

E-SIGN Applies to Assignments of Copyrights and Overrides § 204

AHRN challenged MRIS’s ownership of the photographs by arguing that an MLS subscriber’s electronic agreement to MRIS’s Terms of Use does not operate as an assignment of rights under § 204, which requires a signed “writing.”

In a bad sign for AHRN, the court began its discussion by volunteering an argument that MRIS did not even bring up:

    [I]n situations where “the copyright [author] appears to have no dispute with its [assignee] on this matter, it would be anomalous to permit a third party infringer to invoke [Section 204(a)’s signed writing requirement] against the [assignee].”

With that in mind, the court went on to discuss the E-SIGN act’s impact on the conveyance of copyrights. After establishing the meaning of “e-signature,” the court focused on whether the act was limited from covering this type of situation.

    The Act provides that it “does not . . . limit, alter, or otherwise affect any requirement imposed by a statute, regulation, or rule of law . . . other than a requirement that contracts or other records be written, signed, or in nonelectric form[.]”

The court emphasized the phrase “other than,” reasoning that a plain reading of the E-SIGN language showed that Congress intended the provisions to limit § 204. It also noted that Congress did not list copyright assignments among the various agreements to which E-SIGN did not apply–nor was there a catchall that included such assignments.

The court then turned to the Hermosilla case, in which a district court in Florida upheld the validity of a copyright conveyance via e-mail. It emphasized the Hermosilla court’s reliance on the purpose of § 204–“to resolve disputes between copyright owners and transferees and to protect copyright holders from persons mistakenly or fraudulently claiming oral licenses or copyright ownership.” The appellate court agreed with the Hermosilla court that allowing assignment via e-mail actually helped cut down on these types of disputes.

    To invalidate copyright transfer agreements solely because they were made electronically would thwart the clear congressional intent embodied in the E-Sign Act.

All in all, the court basically said “we don’t see why E-SIGN shouldn’t apply.” Note that it did not pass judgment specifically on whether MRIS’s Terms of Use constituted a valid contract. It simply mentioned that AHRN waived that argument by not bringing it up sooner.


Monday, 1 December 2014

The Roots of Web Scraping and the Wisdom behind It

You may be wondering how data mining came into existence. This effective and innovative trend in business and research is indeed something commendable and the genius behind it is worth great reward. To have a clear view of the origin of web scraping, the following important factors that contribute to the creation of this phenomenon called data collection or web scraping are considered.


Unlike any other innovation, no specific date can be clearly pointed out as the birthdate of data mining. It has come into existence as a result of several problem solving processes in major data gathering and handling situations. It appears that cyber technology has opened a Pandora box of “anything can happen” experiences. Moreover, the shift from physical to virtual data collection has resulted in a bulk of database that needed to be organized, analyzed and utilized.


Friday, 28 November 2014

Scraping Online Communities for your Outreach Campaigns

Online communities offer a wealth of intelligence for blog owners and business owners alike.

Exploring the data within popular communities will help you to understand who the major influencers are, what content is popular and who are the key content aggregators within your niche.

This is all fair and well to talk about, but is it feasible to be manually sorting through online communities to find this information out? Probably not.

This is where data scraping comes in.

What is Scraping and What Can it do?

I’m not going to go into great detail on what data scraping actually means, but to simplify this, here’s a definition from the Wikipedia page:

    “Data scraping is a technique in which a computer program extracts data from human-readable output coming from another program.”

Let me explain this with a little example…

Imagine a huge community full of individuals within your industry. Each person within the community has a personal profile page that contains information about their interests, contact details, social profiles, etc.

If you were tasked with gathering all of this data on all of the individuals then you might start to hyperventilating at the thought of all the copy and pasting you’d need to do.

Well, an alternative is to scrape all of this content so that you can automate all of this process and easily export all of this information into a manageable, more consumable format in a matter of seconds. It’d be pretty awesome, right?

Luckily for you, I’m going to show you how to do just that!

The Example of

Recently, I wanted to gather a list of digital marketers that were fairly active on social media and shared a lot of content online within communities. These people were going to be some of my core targets to get content from the blog in front of.

To do this, I first found some active communities online where these types of individuals hang out. Being a digital marketer myself, this process was fairly easy and I chose as my starting place.

Scoping out Data Requirements

Each community is different and you’ll be able to gather varying information within each.

The place to look for this information is within the individual user profile pages. This is usually where the contact information or links to social media accounts are likely to be displayed.

For this particular exercise, I wanted to gather the following information:

    Full name
    Job title
    Company name and URL
    Personal website URL
    Twitter URL, handle and follower/following stats
    Google+ URL, follower count and list of contributor URLs
    Profile image URL
    Facebook URL
    LinkedIn URL

With all of this information I’ll be able to get a huge amount of intelligence about the community members. I’ll also have a list of social media accounts to add and engage with.
On top of this, with all the information on their websites and sites that they write for, I’ll have a wealth of potential link building prospects to work on. Profiles

You’ll see in the above screenshot that a few of the pieces of data are available to see on the user profiles. We’ll need to get the other bits of information from the likes of Twitter and Google+, but this will all stem from the scraping of

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Scraping the Data

The idea behind this is that we can set up a template based on one of the user profiles and then automate the data gathering across the rest of the profiles on the site.

This is where you’ll need to install the SEO Tools plugin for Excel (it’s free). If you’ve not used this plugin before, don’t worry – I’ve put together a full tutorial here.

Once you’ve installed the plugin, you’re good to go on the actual scraping side of things…

Quick Note: Don’t worry if you don’t have a good knowledge of coding – you don’t need it. All you’ll need is a very basic understanding of reading some code and some basic Excel skills.

To begin with, you’ll need to do a little Excel admin. Simply add in some column titles based around the data that you’re gathering. For example, with my example of, I had, ‘Name’, ‘Position’, ‘Company’, ‘Company URL’, etc. which you can see in the screenshot below. You’ll also want to add in a sample profile URL to work on building the template around.

spreadsheet admin

Now it’s time to start getting hands on with XPath.

How to Use XPathOnURL()

This handy little formula is made possible within Excel by the SEO Tools plugin. Now, I’m going to keep this very basic because there are loads of XPath tutorials available online that can go into the very advanced queries that are possible to use.

For this, I’m simply going to show you how to get the data we want and you can have a play around yourself afterwards (you can download the full template at the end of this post).

Here’s an example of an XPath query that gathers the name of the person within the profile that we’re scraping:

=XPathOnUrl(A2, "//*[@id='user-profile']/h2")

A2 is simply referencing the cell that contains the URL that we’re scraping. You’ll see in the screenshot above that this is Jason Acidre’s profile page.

The next part of the formula is the XPath.

What this essentially says is to scrape through the HTML to find a tag that has ‘user-profile’ id attached to it. This could be a div, span, a or whatever.

Once it’s found this tag, it then needs to look at the first h2 tag within this area and grab the text within it. This is Jason’s name, which you’ll see in the screenshot below of the code:

website code

Don’t be put off at this stage because you don’t need to go manually trawling through code to build these queries, there’s a much simpler way.

The easiest way to do this is by right-clicking on the element you want to scrape on the webpage (within Chrome); for example, on, this would be the profile name. Now click ‘Inspect element’.

inspect element

The developer tools window should now appear at the bottom of your browser (or in a separate window). Within that, you should see the element that you’ve drilled down on.

All you need to do now is right-click on it and press ‘Copy XPath’.

copy XPath

This will now copy the XPath code for your Excel formula to the clipboard. You’ll just need to add in the first part of the query, i.e. =XPathOnUrl(A2,

You can then paste in the copied XPath after this and add a closing bracket.

Note: When you use ‘Copy XPath’ it will wrap some parts of the code in double apostrophes (“) which you’ll need to change to single apostrophes. You’ll also need to wrap the copied XPath in double apostrophes.

Your finished code will look like this:

=XPathOnUrl(A2, "//*[@id='user-profile']/h2")

You can then apply this formula against any profile and it will automatically grab the user’s full name. Pretty good, right?

Check out the full video tutorial below that I’ve put together that talks you through this whole process:

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XPath Examples for Grabbing Other Data

As you’re probably starting to see, this technique could be scaled across any website online. This makes big data much more attainable and gives you the kind of results that an expensive paid tool would offer without any of the cost – bonus!

Here’s a few more examples of XPath that you can use in conjunction with the SEO Tools plugin within Excel to get some handy information.

Twitter Follower Count

If you want to grab the number of followers for a Twitter user then you can use the following formula. Simply replace A2 with the Twitter profile URL of the user you want data on. Just a quick word of warning with this one; it looks like it’s really long and complicated, but really I’ve just used another Excel formula to snip of the text ‘followers’ from the end.

=RIGHT(XPathOnUrl(D57,"//li[@class='ProfileNav-item ProfileNav-item--followers']"),LEN(XPathOnUrl(D57,"//li[@class='ProfileNav-item ProfileNav-item--followers']"))-10)

Google+ Follower Count

Like with the Twitter follower formula, you’ll need to replace A2 with the full Google+ profile URL of the user you want this data for.


List of ‘Contributor to’ URLs

I don’t think I need to tell you the value of pulling in a list of websites that someone contributes content to. If you do want to know then check out this post that I wrote.

This formula is a little more complex than the rest. This is because I’m pulling in a list of URLs as opposed to just one entity. This requires me to use the StringJoin function to separate all of the outputs with a comma (or whatever character you’d like).

Also, you may notice that there is an additional section to the XPath query, “href”. This pulls in the link within the specific code block instead of the text.

As you’ll see in the full scraper template that I’ve made, this is how I pull in the Twitter, Google+, Facebook and LinkedIn profile links.

You’ll want to replace A2 with the Google+ profile URL of the person you wish to gather data on.

=StringJoin(", ",XPathOnUrl(A2,"//a[@rel='contributor-to nofollow']","href"))

Twitter Profile Image URL

If you want to get a large version of someone’s Twitter profile image then I’ve got just the thing for you.

Again, you’ll just need to substitute A2 with their Twitter profile URL.

=XPathOnUrl(A2,"//*[@class='profile-picture media-thumbnail js-tooltip']","data-resolved-url-large")

Some Findings from the Data I’ve Gathered

With all big data sets will come some interesting findings. Here’s a few little things that I’ve found from the top 100 influential users on

average followers chart

The chart above maps out the average number of followers that the top 100 users have on both Twitter (12,959) and Google+ (9,601). As well as this, it shows the average number of users that they follow on Twitter (1,363).

The next thing that I’ve looked at is the job titles of the top 100 users. You can see the most common occurrences of terms within the tag cloud below:

Job titlesFinally, I had a look through all of the domains listed within each of the top 100 users’ Google+ ‘contributor to’ sections and mapped out the most frequently mentioned sites.

Here’s the spread of domains that were the most popular to be contributed to:

domain frequency

It Doesn’t Stop There

As you’ve probably gathered, this can be scaled out across pretty much any community/forum/directory/website online.

With this kind of intelligence in your armoury, you’ll be able to gather more intelligence on your targets and increase the effectiveness of your outreach campaigns dramatically.

Also, as promised, you can download my full scraper template below:

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    Online communities hold valuable data on your target audiences – use it!

    Scale out your intelligence gathering by brushing up on your XPath.

    Download my scraper template and let it work its magic.


Thursday, 27 November 2014

Web Data Extraction: driving data your way

Most businesses rely on the web to gather data such as product specifications, pricing information, market trends, competitor information, and regulatory details. More often than not, companies collect this data manually—a process that not only takes a significant amount of time, but also has the potential to introduce costly errors.

By automating data extraction, you're able to free yourself (and your pointer finger) from hours of copy/pasting, eliminate human errors, and focus on the parts of your job that make you feel great.

Web data extraction: What it is, why it's used, and how to get it right on an ongoing basis

Web data extraction, screen scraping, web harvesting—while these terms may have different connotations they all essentially point to the same thing: plucking data from the web and populating it in an organized way in another place for further analysis or more focused use. In an era where “big data” has become a commonplace concept, the appeal of web data extraction has grown; it’s an extremely efficient alternative to web browsing, and culls very specific data for a focused purpose.

How it's used

While each company’s needs vary, data extraction is often used for:

    Competitive intelligence, including web popularity, social perception, other sites linking to them, and placement of competitor advertisements

    Gathering financial data including stock market movement, product pricing, and more

    Creating continuity between price sheets and online websites, catalogs, or inventory databases

    Capturing product specifications like dimensions, color, and materials

    Pulling tabular data from multiple sources for in-depth analysis

Interestingly, some people even find that web data extraction can aid them in their leisure time as well, pulling data from blogs and websites that pertain to their hobbies or interests, and creating their own library of organized information on a topic. Perhaps, for instance, you want a list of all the designers that George Clooney wears (hey- we won’t question what you do in your free time). By using web scraping tools, you could automatically extract this type of data from, say, a fashion blogger who follows celebrity style, and create your own up-to-date shopping list of items.

How it's done

When you think of gathering data from the web, you should mentally juxtapose two different images: one of gathering a bucket of sand one grain at a time, and one of filling a bucket with a shovel that has the perfect scoop size to fill it completely in one sitting. While clearly the second method makes the most sense, the majority of web data extraction happens much like the first process--manually, and slowly.

Let’s take a look at a few different ways organizations extract data today.

The least productive way: manually

While this method is the least efficient, it’s also the most widespread. On the plus side, you need to learn absolutely nothing except “Ctrl+C/V” to use this method, which explains why it is the generally preferred method, despite the hours of time it can take. Imagine, for instance, managing a sales spreadsheet that keeps inventory up to date so that the information can be properly disseminated to a global sales team. Not only does it take a significant amount of time to update the spreadsheet with information from, say, your internal database and manufacturer’s website, but information may change rapidly, leaving sales reps with inaccurate information regardless.

Finding someone in the organization with a talent for programming languages like Python

Generally, automating a task without dedicated automation software requires programming, and therefore an internal resource with a solid familiarity with programming languages to create the task and corresponding script. While most organizations do, in fact, have a resource in IT or engineering with this type of ability, it often doesn’t seem like a worthy time investment for that person to derail the initiatives he or she is working on to automate web data extraction. Additionally, if companies do choose to automate using in-house resources, that person will find himself beholden to a continuing obligation, since he or she will need to adjust scripting if web objects and attributes change, disabling the task.

Outsourcing via Elance or oDesk

Unless there is a dedicated resource ready to automate and maintain data extraction processes (and most organizations wouldn’t necessarily choose to use their in-house employee time this way), companies might turn to outsourcing companies such as Elance or oDesk to hire contract help. While this is an effective way to automate a task using a resource that has a level of acumen in automation, it represents an additional cost--be it one time or on a regular basis as data extraction requirements change or increase.

Using Excel web queries

Since more often than not, data extracted from the web is often populated into an Excel spreadsheet, it’s no wonder that Excel includes web query tools expressly for that purpose. These tools are particularly useful in pulling tabular data from a website (such as product specifications, legal codes, stock prices, and a host of other information) and automatically pushing the data into a spreadsheet. Excel queries do have limitations and a learning curve, however, particularly when creating dynamic web queries. And clearly, if you’re hoping to populate the information in other sources, such as external databases, there is yet another level of difficulty to navigate.

How automation simplifies web data extraction

Culling web data quickly

Using automation is the simplest way to extract web data. As you execute the steps necessary to perform the task one time, a macro recorder captures each action, automatically generates an easily-editable script, and lets you specify how often you would like to repeat the task, and at what speed.

Maintaining the highest level of accuracy

With humans copy/pasting data, or comparing between multiple screens and entering data manually into a spreadsheet, you’re likely to run into accuracy issues (sometimes directly proportionate to the amount of time spent on the task and amount of coffee in the office!) Automation software ensures that “what you see is what you get,” and that data is picked up from the web and put back down where you want it without a hitch.

Storing web data in your preferred format

Not only can you accurately transfer data with automation software, you can also ensure that it’s populated into spreadsheets or databases in the format you prefer. Rather than simply dumping the data into a spreadsheet, you can ensure that the right information is put into the proper column, row, field, and style (think, for instance, of the difference between writing a birth date as “03/13/1912” and “12/3/13”).

Simplifying data analysis

Automation software allows you to aggregate data from disparate sources or enormous stockpiles of structured or unstructured data in a way that makes sense for your business analysis needs. This way, the majority of employees in an organization can perform some level of analysis on their own, making it easier to surface information that informs business decisions.

Reacting to changes without a hitch

Because automation software is built to recognize icons, images, symbols, and other objects regardless of their position on a screen, it can automate processes in a self-perpetuating manner. For example, let’s say you automate data retrieval from a certain chart on a retailer’s website without automation software. If the retailer decides to move that object to another area of the screen, your task would no longer produce accurate results (or work at all), leaving you to make changes to the script (or find someone who can), or re-record the task altogether. With image recognition capabilities, however, the system “memorizes” the object itself, not merely its coordinates, so that the task can continue to run irrespective of changes.

The wide sweeping appeal of automation software

Companies often pick a comprehensive automation solution not only because of its ability to effectively automate any web data extraction task, but also because it goes beyond data extraction. Automation software can permeate into other areas of the business as well, making tasks such as application integration, data migration, IT processes, Excel automation, testing, and routine tasks such as launching applications or formatting files faster and more accurate. Because it requires no programming experience to use, adoption rates are higher and businesses get more “bang for their buck.”

Almost any organization can benefit from using automation software, particularly as they grow and scale. If you are looking to quit “moving grains of sand” and start claiming back time in your day, there are a few steps you can take:

 Watch a short video that shows how web data extraction is done with automation software

 Download a free trial and start reaping the benefits of downloading even just a couple of tasks today.

 See how tasks are automated with our short, step-by-step how-to-sheets (and then give it a try yourself!)


Tuesday, 25 November 2014

Outsourcing Data Mining is a Wise Business Decision

Most businesses nowadays have a large volume of raw data that is never processed, because of the lack of time or resources. If your business is facing a similar situation, then you are missing out on valuable information. Without the right information, your company will be unable to make accurate business decisions.

The right information can play a key role in promoting the growth of your business. When unprocessed data is entered, filtered, classified and converted into a workable format, it can be used to maximize your profits, ameliorate your risks and run a seamless workflow.

Over the years, data mining has proved to be extremely useful in various industries, be it, healthcare, direct marketing, e-commerce, finance, customer relationship management or telecommunications. With the right information, companies have been able to make fast and effective business decisions.

Why outsource data mining?

Data mining requires the expertise of professional business and financial analysts who understand how to acquire important information from vast amounts of data. If data mining is done in-house, it can become expensive and time consuming. It can also shift your focus away from core business activities. Outsourcing data mining on the other hand is more fast, cost-effective and can give you access to professional services.

4 commonly outsourced data mining functions

Most companies outsource one or more of the following data mining functions to India:

1. Data congregation: Data is extracted from various web pages and websites, by using methods like web and screen scraping. The collected data is then entered into a database.

2. Contact data collection: Different websites are searched and information concerning contacts is collected.

3. E-commerce data: Data about varied online stores are collected, taking into account information about prices, discounts and products.

4. Data about competitors: Data about business competitors are collected to help a company gauge itself against its competition. With such valuable data, you can effectively re-design your marketing strategy and pricing matrix.

8 advantages of outsourcing data mining to India

With data mining out of your hands, your business can make huge savings in terms of time, money and infrastructure. The following are some of the benefits that you can leverage by outsourcing data mining to India:

    Get qualified and highly skilled data mining experts to work for you at an extremely affordable cost

    Be assured of the quality of information, as Indian data entry companies only extract information from reliable websites and databases

    Save on the cost of investing on the latest data mining software and technology, as your Indian service provider will be making these investments

    Get your data processed within a short turnaround time of 3,6 or 12 hours as Indian data mining companies can provide efficient data mining within a few hours

    When compared to in-house data mining, outsourcing data mining can be a lot cheaper and also bring you better results

    Stay assured about the complete privacy, security and confidentiality of your valuable data as Indian data mining companies use the latest technology to ensure 100% safety

    Get access to data with a wide market coverage as your Indian data mining provider will be serving many business with varied data mining needs

    Improve your overall productivity and generate more profits by making informed decisions about your business

Have you outsourced data mining before? If yes, which data mining service did you outsource? Did you find outsourcing more advantageous that in-house data mining. Let us know.


Friday, 21 November 2014

Online Data Entry & Web Scraping Services

To operate any type of organization smoothly, it is essential to have precise data that is accurate and reliable. When your business expands, data entry on an ongoing basis is a tedious job. It’s a very time consuming task that can often distract employees focusing on core business areas.

Webpop offers all forms of online data entry services that are quick and accurate. We provide data entry services across all verticals that can be completely customized to your business requirements.

Database Population Services

Database population involves content collection from various database sources. This requires a lot of attention to detail, dedication and awareness and can prove a formidable task, especially for websites that largeley depend on it.

Webpop offer a quick and efficient database population service that helps relieve the stress from an extremely laborius task and leaves you more time to focus on more important aspects of your business. By investing just a fraction of the cost, you can outsource your database population tasks to us.

Web Scraping Services

Webpop have been assisting clients in searching, extracting and collecting data from the web for the past 5 years using the latest techniques in web scraping techology. We can scrape all types of information from a variety of sources such as websites, blogs, online directories, e-commerce websites and podcasts to name a few. We use a varied selection of automated and manual web scraping technologies to extract, gather and collect all of the required data you require from any chosen website(s) on the World Wide Web.

We can simplify the whole process from collection to population, converting your scraped data in to structured formats that are applicable to your website. This can be offered as a one time service or an ongoing basis that will assist you in constantly keeping your website’s content fresh and up to date. We can crawl competitors websites, gather sales leads, product details, pricing methodologies and also creat custom campaigns to suit your project’s requirements.

Over the years Webpop has grown from strength-to-strength by providing all types of data entry, database population and web scraping services. All of our data entry services are performed with care, due dilligence and attention to detail. We enjoy a challenge and pride ourselves on delivering results whilst working on precarious projects that require precision and total commitment.


Tuesday, 18 November 2014

Kimono Is A Smarter Web Scraper That Lets You “API-ify” The Web, No Code Required

A new Y Combinator-backed startup called Kimono wants to make it easier to access data from the unstructured web with a point-and-click tool that can extract information from webpages that don’t have an API available. And for non-developers, Kimono plans to eventually allow anyone track data without needing to understand APIs at all.

This sort of smarter “web scraper” idea has been tried before, and has always struggled to find more than a niche audience. Previous attempts with similar services like Dapper or Needlebase, for example, folded. Yahoo Pipes still chugs along, but it’s fair to say that the service has long since been a priority for its parent company.

But Kimono’s founders believe that the issue at hand is largely timing.

“Companies more and more are realizing there’s a lot of value in opening up some of their data sets via APIs to allow developers to build these ecosystems of interesting apps and visualizations that people will share and drive up awareness of the company,” says Kimono co-founder Pratap Ranade. (He also delves into this subject deeper in a Forbes piece here). But often, companies don’t know how to begin in terms of what data to open up, or how. Kimono could inform them.

Plus, adds Ranade, Kimono is materially different from earlier efforts like Dapper or Needlebase, because it’s outputting to APIs and is starting off by focusing on the developer user base, with an expansion to non-technical users planned for the future. (Meanwhile, older competitors were often the other way around).

The company itself is only a month old, and was built by former Columbia grad school companions Ranade and Ryan Rowe. Both left grad school to work elsewhere, with Rowe off to Frog Design and Ranade at McKinsey. But over the nearly half-dozen or so years they continued their careers paths separately, the two stayed in touch and worked on various small projects together.

One of those was, a website that told you which movies were showing on your flights. This ended up giving them the idea for Kimono, as it turned out. To get the data they needed for the site, they had to scrape data from several publicly available websites.

“The whole process of cleaning that [data] up, extracting it on a schedule…it was kind of a painful process,” explains Rowe. “We spent most of our time doing that, and very little time building the website itself,” he says. At the same time, while Rowe was at Frog, he realized that the company had a lot of non-technical designers who needed access to data to make interesting design decisions, but who weren’t equipped to go out and get the data for themselves.

With Kimono, the end goal is to simplify data extraction so that anyone can manage it. After signing up, you install a bookmarklet in your browser, which, when clicked, puts the website into a special state that allows you to point to the items you want to track. For example, if you were trying to track movie times, you might click on the movie titles and showtimes. Then Kimono’s learning algorithm will build a data model involving the items you’ve selected.

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That data can be tracked in real time and extracted in a variety of ways, including to Excel as a .CSV file, to RSS in the form of email alerts, or for developers as a RESTful API that returns JSON. Kimono also offers “Kimonoblocks,” which lets you drop the data as an embed on a webpage, and it offers a simple mobile app builder, which lets you turn the data into a mobile web application.

Screen Shot 2014-02-18 at 4.29.50 PM

For developer users, the company is currently working on an API editor, which would allow you to combine multiple APIs into one.

So far, the team says, they’ve been “very pleasantly surprised” by the number of sign-ups, which have reached ten thousand*. And even though only a month old, they’ve seen active users in the thousands.

Initially, they’ve found traction with hardware hackers who have done fun things like making an airhorn blow every time someone funds their Kickstarter campaign, for instance, as well as with those who have used Kimono for visualization purposes, or monitoring the exchange rates of various cryptocurrencies like Bitcoin and dogecoin. Others still are monitoring data that’s later spit back out as a Twitter bot.

Kimono APIs are now making over 100,000 calls every week, and usage is growing by over 50 percent per week. The company also put out an unofficial “Sochi Olympics API” to showcase what the platform can do.

The current business model is freemium based, with pricing that kicks in for higher-frequency usage at scale.

The Mountain View-based company is a team of just the two founders for now, and has initial investment from YC, YC VC and SV Angel.


Monday, 17 November 2014

A Web Scraper’s Guide to Kimono

Being a frequent reader of Hacker News, I noticed an item on the front page earlier this year which read, “Kimono – Never write a web scraper again.” Although it got a great number of upvotes, the tech junta was quick to note issues, especially if you are a developer who knows how to write scrapers. The biggest concern was a non-intuitive UX, followed by the inability of the first beta version to extract data items from websites as smoothly as the demo video suggested.

I decided to give it a few months before I tested it out, and I finally got the chance to do so recently.

Kimono is a Y-Combinator backed startup trying to do something in a field where others have failed. Kimono is focused on creating APIs for websites which don’t have one, another term would be web scraping. Imagine you have a website which shows some data you would like to dynamically process in your website or application. If the website doesn’t have an API, you can create one using Kimono by extracting the data items from the website.

Is it Legal?

Kimono provides an FAQ section, which says that web scraping from public websites “is 100% legal” as long as you check the robots.txt file to see which URL patterns they have disallowed. However, I would advise you to proceed with caution because some websites can pose a problem.

A robots.txt is a file that gives directions to crawlers (usually of search engines) visiting the website. If a webmaster wants a page to be available on search engines like Google, he would not disallow robots in the robots.txt file. If they’d prefer no one scrapes their content, they’d specifically mention it in their Terms of Service. You should always look at the terms before creating an API through Kimono.

An example of this is Medium. Their robots.txt file doesn’t mention anything about their public posts, but the following quote from their TOS page shows you shouldn’t scrape them (since it involves extracting data from their HTML/CSS).

    For the remainder of the site, you may not duplicate, copy, or reuse any portion of the HTML/CSS, JavaScipt, logos, or visual design elements without express written permission from Medium unless otherwise permitted by law.

If you check the #BuiltWithKimono section of their website, you’d notice a few straightforward applications. For instance, there is a price comparison API, which is built by extracting the prices from product pages on different websites.

Let us move on and see how we can use this service.

What are we about to do?

Let’s try to accomplish a task, while exploring Kimono. The Blog Bowl is a blog directory where you can share and discover blogs. The posts that have been shared by users are available on the feeds page. Let us try to get a list of blog posts from the page.

The simple thought process when scraping the data is parsing the HTML (or searching through it, in simpler terms) and extracting the information we require. In this case, let’s try to get the title of the post, its link, and the blogger’s name and profile page.


Friday, 14 November 2014

Future of Web Scraping

The Internet is large, complex and ever-evolving. Nearly 90% of all the data in the world has been generated over the last two years. In this vast ocean of data, how does one get to the relevant piece of information? This is where web scraping takes over.

Web scrapers attach themselves, like a leech, to this beast and ride the waves by extracting information form websites at will. Granted “scraping” doesn’t have a lot of positive connotations, yet it happens to be the only way to access data or content from a web site without RSS or an open API.

Future of Web Scraping

Web scraping faces testing times ahead. We outline why there may be some serious challenges to its future.

With rise in data, redundancies in web scraping are rising. No more is web scraping a domain of the coders; in fact, companies now offer customized scraping tools to clients which they can use to get the data they want. The outcome of everyone equipped to crawl, scrape, and extract, is unnecessary waste of precious man-power. Collaborative scraping could well heal this hurt. Here, where one web crawler does a broad scraping, the others scrape data off an API. An extension of the problem is that text retrieval attracts more attention than multimedia; and with websites becoming more complex, this enforces limited scraping capacity.

Easily, the biggest challenge to web scraping technology is Privacy concerns. With data freely available (most of it voluntary, much of it involuntary), the call for stricter legislation rings loudest. Unintended users can easily target a company and take advantage of the business using web scraping. The disdain with which “do not scrape” policies are treated and terms of usage violated, tells us that even legal restrictions are not enough. This begs to ask an age-old question: is scraping legal?

Is Crawling Legal? from PromptCloud

The flipside to this argument is that if technological barriers replace legal clauses, then web scraping will see a steady, and sure, decline. This is a distinct possibility since the only way scraping activity thrives is on the grid, and if the very means are taken away and programs no longer have access to website information, then web scraping by itself will be wiped out.

Building the Future

On the same thought is the growing trend of accepting “open data”. The open data policy, while long mused hasn’t been used at the scale it should be. The old way was to believe that closed data is the edge over competitors. But that mindset is changing. Increasingly, websites are beginning to offer APIs and embracing open data. But what’s the advantage of doing so?

Selling APIs not only brings in the money, but also is useful in driving back traffic to the sites! APIs are also a more controlled, cleaner way of turning sites into services. Steadily many successful sites like Twitter, LinkedIn etc. are offering access to their APIs with paid services and actively blocking scraper and bots.

Yet, beyond these obvious challenges, there’s a glimmer of hope for web scraping. And this is based on a singular factor: the growing need for data!

With Internet & web technology spreading, massive amounts of data will be accessible on the web. Particularly with increased adoption of mobile internet. According to one report, by 2020, the number of mobile internet users will hit 3.8 billion, or around half of the world’s population!

Since ‘big data’ can be both, structured & unstructured; web scraping tools will only get sharper and incisive. There is fierce competition between those who provide web scraping solutions. With the rise of open source languages like Python, R & Ruby, Customized scraping tools will only flourish bringing in a new wave of data collection and aggregation methods.