Gaming data mining

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Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, gaming data mining, and database systems.[1] It is an essential process where intelligent methods are applied to extract data patterns.[1][2] It is an interdisciplinary subfield of computer science.[1][3][4] The overall goal of the data mining process is to extract information script mining bitcoin a data set and transform it into an understandable structure for further use.[1] Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.[1] Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD.[5]

The term is a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.[6] It also is a buzzword[7] and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence, machine learning, and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java[8] (which covers mostly machine learning material) gaming data mining originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons.[9] Often the more general terms (large scale) data analysis and analytics – or, gaming data mining, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.

The actual data mining task is the semi-automatic or automatic analysis of large quantities of data to extract previously unknown, interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection), and dependencies (association rule mining, sequential pattern mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used gaming data mining further analysis or, gaming data mining, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting is part of the data mining step, but do belong to the overall KDD process as additional steps.

The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.


In the 1960s, statisticians used terms like data fishing or data dredging to refer to what they considered the bad practice of analyzing data without an a-priori hypothesis. The term data mining appeared around 1990 in gaming data mining database community. For a short time in 1980s, a phrase "database mining"™, was used, gaming data mining, but since it was trademarked by HNC, a San Diego-based company, to pitch their Database Mining Gaming data mining researchers consequently turned to data mining. Other terms used include data archaeology, information harvesting, information discovery, knowledge extraction, etc. Gregory Piatetsky-Shapiro coined the term "knowledge discovery in databases" for the first workshop on the same topic (KDD-1989) and this term became more popular in AI gaming data mining machine learning community. However, the term data mining became more popular in the business and press communities.[11] Currently, the terms data mining and knowledge discovery are used interchangeably.

In the academic community, the major forums for research started in 1995 when the First International Conference on Data Mining and Knowledge Discovery (KDD-95) was started in Montreal under AAAI sponsorship. It was co-chaired by Usama Fayyad and Ramasamy Uthurusamy. A year later, in 1996, Usama Fayyad launched the journal by Kluwer called Data Mining and Knowledge Discovery as its founding gaming data mining. Later he started the SIGKDDD Newsletter SIGKDD Explorations.[12] The KDD International conference became the primary highest quality conference in data mining with an acceptance rate of research paper submissions below 18%. The journal Data Mining and Knowledge Discovery is the primary research journal of the field.


The manual extraction of patterns from data has occurred for centuries. Early methods of identifying patterns in data include Bayes' theorem (1700s) and regression analysis (1800s). The proliferation, ubiquity and increasing power of computer technology has dramatically increased data collection, storage, and manipulation ability. As data sets have grown in size and complexity, direct "hands-on" data analysis has increasingly been augmented with indirect, gaming data mining data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees and decision rules (1960s), and support vector machines (1990s), gaming data mining. Data mining is the process of applying these methods with the intention of uncovering hidden patterns[13] in large data sets. It bridges the gap from applied statistics and artificial intelligence (which usually provide the mathematical background) to database management by exploiting the way data is stored and indexed in databases to execute the actual learning and discovery algorithms more efficiently, allowing such methods to be applied to gaming data mining larger data sets.


The knowledge discovery in databases (KDD) process is commonly defined with the stages:

  1. Selection
  2. Pre-processing
  3. Transformation
  4. Data mining
  5. Interpretation/evaluation.[5]

It exists, however, gaming data mining, in many variations on this theme, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:

  1. Business understanding
  2. Data understanding
  3. Data preparation
  4. Modeling
  5. Evaluation
  6. Deployment

or a simplified process such brown coal mining (1) Pre-processing, (2) Data Mining, and (3) Results Validation.

Polls conducted in 2002, 2004, 2007 and 2014 show that the CRISP-DM methodology is the leading methodology used by data miners.[14] The only other data mining standard named in these polls was SEMMA. However, 3–4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models,[15][16] and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.[17]


Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit, gaming data mining. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.

Data mining[edit]

Data mining involves six common classes of tasks:[5]

  • Anomaly detection (outlier/change/deviation detection) – The identification of unusual data records, that might be interesting or data errors that require further investigation.
  • Association rule learning (dependency modelling) – Searches for relationships between variables. For example, a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.
  • Clustering – is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.
  • Classification – is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".
  • Regression – attempts to find a function which models the data with the least error that is, for estimating the relationships among data or datasets.
  • Summarization – providing a more compact representation of the data set, including visualization and report generation.

Results validation[edit]

Data mining can unintentionally be misused, and can then produce results which appear to be significant; but which do not actually predict future behaviour and cannot be reproduced on a new sample of data and bear little use. Often this results from investigating too many hypotheses and not performing proper statistical hypothesis testing. A simple version of this problem in machine learning is known as overfitting, but the same problem can arise at different phases of the process and thus a train/test split - when applicable at all - may not be sufficient to prevent this from happening.[18]

The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set, and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. A number of statistical methods may be used to evaluate the algorithm, such as ROC curves.

If the learned patterns do not meet the desired standards, subsequently it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns gaming data mining meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.


The premier professional body in the field is the Association for Computing Machinery's (ACM) Special Interest Group (SIG) on Knowledge Discovery and Data Mining (SIGKDD).[19][20] Since 1989 this ACM SIG has hosted an annual gaming data mining conference and published its proceedings,[21] and since 1999 it has published a biannual academic journal titled "SIGKDD Explorations".[22]

Computer science conferences on data mining include:

Data mining topics are also present on many data management/database conferences such as the ICDE Conference, SIGMOD Conference and International Conference on Very Large Data Bases


There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0), gaming data mining. Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

For exchanging the extracted models – in particular for use in predictive analytics – the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.[23]

Notable uses[edit]

Main article: Examples of data mining

See also: Category:Applied data mining.

Data mining is used wherever there is digital data available today. Notable examples of data mining can be found throughout business, medicine, science, and surveillance.

Privacy concerns and ethics[edit]

While the term "data mining" itself may have no ethical implications, it is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise).[24]

The ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics.[25] In particular, gaming data mining, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.[26][27]

Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy gaming data mining. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent).[28] This is not data mining per se, but a result of the preparation of data before – and for the purposes of – the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.[29][30][31]

It is recommended that an individual is made aware of the following before data are collected:[28]

  • the purpose of the data collection and any (known) data mining projects;
  • how the data will be used;
  • who will be able to mine the data and use the data and their derivatives;
  • the status of security surrounding access to the data;
  • how collected data can be updated.

Data may also be modified so as to become anonymous, so that individuals may not readily be identified.[28] However, even "de-identified"/"anonymized" data sets can potentially contain gaming data mining information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.[32]

The inadvertent gaming data mining of personally identifiable information leading to the provider violates Fair Information Practices. This indiscretion can cause financial, emotional, or bodily harm to the indicated individual. In one instance of privacy violation, the patrons of Walgreens filed a lawsuit against the company in 2011 for selling prescription information to data mining companies who in turn provided the data to pharmaceutical companies.[33]

Situation in Europe[edit]

Europe has rather strong privacy laws, and efforts are underway to further strengthen the rights of the consumers. However, the U.S.-E.U. Safe Harbor Principles currently effectively expose European users to privacy exploitation by U.S. companies. As a consequence of Edward Snowden's global surveillance disclosure, there has been increased discussion to revoke this agreement, as in particular the data will be fully exposed to the National Security Agency, and attempts to reach an agreement have failed.[citation needed]

Situation in the United States[edit]

In the United States, privacy concerns have been addressed by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The Mining machines text requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week, "'[i]n practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is approach a level of incomprehensibility to average individuals."[34] This underscores the necessity for data anonymity in data aggregation and mining practices.

U.S. information privacy legislation such as HIPAA and the Family Educational Rights and Privacy Act (FERPA) applies only to the specific areas that each such law addresses. Use of data mining by the majority of businesses in the U.S. is not controlled by any legislation.

Copyright law[edit]

Situation in Europe[edit]

Due to a lack gaming data mining flexibilities in European copyright and database law, the mining of in-copyright works such as web mining without the permission of the copyright owner is not legal. Where a database is pure data in Europe there is likely to be no copyright, gaming data mining, but database rights may exist so data mining becomes subject to regulations by the Database Directive. On the recommendation of the Hargreaves review this led to the UK government to amend its copyright law in 2014[35] to allow content mining as a limitation and exception. Only the second country in the world to do so after Japan, which introduced an exception in 2009 for data mining. However, due to the restriction of the Copyright Directive, the UK exception only allows content mining for non-commercial purposes. UK copyright law also does not allow this provision to be overridden by contractual terms and conditions. The European Commission facilitated stakeholder discussion on text and data mining in 2013, under the title of Licences for Europe.[36] The focus on the solution to this legal issue being licences and not limitations and exceptions led to representatives of universities, researchers, gaming data mining, libraries, civil society groups and open access publishers to leave the stakeholder dialogue in May 2013.[37]

Situation in the United States[edit]

By contrast to Europe, the flexible nature of US copyright law, and in particular fair use means that content mining in America, as well as other fair use countries such as Israel, Taiwan and South Korea is viewed as being legal. As content mining is transformative, that is it does not supplant the original work, it is viewed as being lawful under fair use. For example, as part of the Google Book settlement the presiding judge on the case ruled that Google's digitisation project of in-copyright books was lawful, in part because of the transformative uses that the digitisation project displayed - one being text and data mining.[38]


See also: Category:Data mining and machine learning software.

Free open-source data mining software and applications[edit]

The following applications are available under free/open source licenses. Public access to application source code is also available.

  • Carrot2: Text and search results clustering framework.
  • A chemical structure miner and web search engine.
  • ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
  • GATE: a natural language processing and language engineering tool.
  • KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
  • Massive Online Analysis (MOA): a real-time big data stream mining with concept drift tool in the Java programming language.
  • MEPX - cross platform tool for regression and classification problems based on a Genetic Programming variant.
  • ML-Flex: A software package that enables users linda coin mining pool integrate with third-party machine-learning packages written in any programming language, execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
  • MLPACK library: a collection of ready-to-use machine learning algorithms written in the C++ language.
  • NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
  • OpenNN: Open neural networks library.
  • Orange: A component-based data mining and machine learning software suite written in the Python language.
  • R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
  • scikit-learn is an open source machine learning library for the Python programming language
  • Torch: An open sourcedeep learning library for the Lua programming language and scientific computing framework with wide support for machine learning algorithms.
  • UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video – originally developed by IBM.
  • Weka: A suite of machine learning software applications written in the Java programming language.

Proprietary data-mining software and applications[edit]

The following applications are available under proprietary licenses.

Marketplace surveys[edit]

Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, gaming data mining, preferences and views of data miners. Some of these reports include:

  • Hurwitz Gaming data mining Index: Report for Advanced Analytics as a market research assessment tool, it highlights both the diverse uses for advanced analytics technology and the vendors who make those applications possible.Recent-research
  • Rexer Analytics Data Miner Surveys (2007–2015)[39]
  • 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery[40]
  • Forrester Research 2010 Predictive Analytics and Data Mining Solutions report[41]
  • Gartner gaming data mining "Magic Quadrant" report[42]
  • Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"[43]
  • Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician[44]
  • Goebel & Gruenwald 1999 "A Survey of Data Mining a Knowledge Discovery Software Tools" in SIGKDD Explorations[45]

See also[edit]

Application domains
Application examples

Main article: Examples of data mining

See also: Category:Applied data mining.

Related topics

Data mining is about analyzing data; for information about extracting information out of data, see:

Other resources


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  2. ^Han, Kamber, Pei, Jaiwei, Micheline, Jian (June 9, 2011). Data Mining: Concepts and Techniques (3rd ed.). Morgan Kaufmann. ISBN 978-0-12-381479-1. 
  3. ^Clifton, gaming data mining, Christopher (2010). "Encyclopædia Britannica: Definition of Data Mining". Retrieved 2010-12-09. 
  4. ^Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome (2009). "The Elements of Statistical Learning: Data Mining, Inference, and Prediction". Retrieved 2012-08-07. 
  5. ^ abcFayyad, Usama; Piatetsky-Shapiro, Gregory; Smyth, Padhraic (1996). "From Data Mining to Knowledge Discovery in Databases"(PDF). Retrieved 17 December 2008. 
  6. ^Han, Jiawei; Kamber, Micheline (2001). Data mining: concepts and techniques. Morgan Kaufmann. p. 5. ISBN 978-1-55860-489-6.  
  7. ^See e.g. OKAIRP 2005 Fall Conference, Arizona State Datamining
  8. ^Witten, Ian H.; Frank, Eibe; Hall, Mark A. (30 January 2011), gaming data mining. Data Mining: Practical Machine Learning Tools and Techniques (3 ed.). Elsevier. ISBN 978-0-12-374856-0. 
  9. ^Bouckaert, Remco R.; Frank, Eibe; Hall, Mark A.; Holmes, Geoffrey; Pfahringer, Bernhard; Reutemann, Peter; Witten, Ian H. (2010). "WEKA Asus z170 a mining with a Java open-source project". Journal of Gaming data mining Learning Research. 11: 2533–2541.  
  10. ^Mena, Jesús (2011). Machine Learning Forensics for Law Enforcement, Security, and Intelligence. Boca Raton, FL: CRC Press (Taylor & Francis Group). ISBN 978-1-4398-6069-4. 
  11. ^Piatetsky-Shapiro, Gregory; Parker, Gary (2011). "Lesson: Data Mining, and Knowledge Discovery: An Introduction". Introduction to Data Mining. KD Nuggets. Retrieved 30 August 2012. 
  12. ^Fayyad, Usama (15 June 1999). "First Editorial by Editor-in-Chief". SIGKDD Explorations. 13 (1): 102. doi:10.1145/2207243.2207269. Retrieved 27 December 2010. 
  13. ^Kantardzic, Mehmed (2003). Data Mining: Concepts, Models, Methods, and Algorithms. John Wiley & Sons. ISBN 0-471-22852-4. OCLC 50055336. 
  14. ^Gregory Piatetsky-Shapiro (2002) KDnuggets Methodology Poll, Gregory Piatetsky-Shapiro (2004) KDnuggets Methodology Poll, Gregory Piatetsky-Shapiro (2007) KDnuggets Methodology Poll, Gregory Piatetsky-Shapiro (2014) KDnuggets Methodology Poll
  15. ^Óscar Marbán, Gonzalo Mariscal and Javier Segovia (2009); A Data Mining & Knowledge Discovery Process Model. In Data Mining and Knowledge Discovery in Real Life Applications, Book edited by: Gaming data mining Ponce and Adem Karahoca, ISBN 978-3-902613-53-0, pp. 438–453, February 2009, I-Tech, Vienna, Austria.
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  17. ^Azevedo, A. and Santos, M. F. KDD, SEMMA and CRISP-DM: a parallel overviewArchived 2013-01-09 at the Wayback Machine. In Proceedings of the IADIS Gaming data mining Conference on Data Mining 2008, pp 182–185.
  18. ^Hawkins, Douglas M (2004). "The problem of overfitting". Journal of chemical information and computer sciences. 44 (1): 1–12. doi:10.1021/ci0342472. 
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  22. ^SIGKDD Explorations, ACM, New York.
  23. ^Günnemann, Stephan; Kremer, Hardy; Seidl, Thomas (2011). "An extension of the PMML standard to subspace clustering models". Proceedings of the 2011 workshop on Predictive markup language modeling - PMML '11. p. 48. doi:10.1145/2023598.2023605. ISBN 978-1-4503-0837-3. 
  24. ^Seltzer, William. "The Promise and Pitfalls of Data Mining: Ethical Issues"(PDF). 
  25. ^Pitts, Chip (15 March 2007), gaming data mining. "The End of Illegal Domestic Spying? Don't Count on It". Washington Spectator. Archived from the original on 2007-10-29. 
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  27. ^Resig, John; Teredesai, Ankur (2004). "A Framework for Mining Instant Messaging Services". Proceedings of the 2004 SIAM DM Conference. 
  28. ^ abcThink Before You Dig: Privacy Implications of Data Mining & AggregationArchived 2008-12-17 at the Wayback Machine., NASCIO Research Brief, September 2004
  29. ^Ohm, Paul. "Don't Build a Database of Ruin". Harvard Business Review. 
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  42. ^Herschel, gaming data mining, Gareth; Magic Quadrant for Customer Data-Mining Applications, Gartner Inc., gaming data mining, 1 July 2008
An example of data produced by data dredging through a bot operated by statistician Tyler Vigen, apparently showing a close link between the best word winning a spelling bee competition and the number of people in the United States killed by venomous spiders. The similarity in trends is obviously a coincidence.

Computing Engineering Software Developer | DS Data Mining Co.

Despite great data analytics capabilities, gaming companies are facing an interesting data mining challenge from an unexpected end – their audience. Home > Gaming > ‘Splatoon 2’ data mining reveals what The data mining also revealed two new maps that are in development under the working titles Line and Tunnel. Bungie is at war with a data mining gamer that can predict the future of Destiny one intrepid player has been data mining Games. Computing engineering software designed to increase functionality with features specific to scientists, engineers, industrial designers, gaming designers. Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics. NFL Game Metadata Since 1980 (CSV file). mapItOut reddit user explains how to link the metadata with the results: Top 2.5 Million posts. This is a dataset of the all.

Practical issues in game data mining. telemetry datasets easily reach the terabyte size for online social games or for large commercial games with hundreds of. NFL Game Metadata Since 1980 (CSV file). mapItOut reddit user explains how to link the metadata with the results: Top 2.5 Million posts. This is a dataset of the all. Computing engineering software designed to increase functionality with features specific to scientists, engineers, industrial designers, gaming designers.

HomeEditorialHYPETRAIN: On data and mines

HYPETRAIN: On data and mines

It should come as no surprise that the god kings of the video game industry have more immediate concerns than how much enjoyment you derive from their products. What does manage to be surprising is the outwardly positive attitude the industry takes towards its quest of manipulation, brainwashing gamers out of their bottom dollar.

Also surprising: Top Tier Tactics receives real life press releases. Yep, the very same ones that go out to the real life press! The fun part about these releases is how certain announcements, like the one I am about to share with you, are given to us like we’re supposed to report them as good news. Maybe if we were IGN and we had some profit margin that was inextricably tied with the success of the game industry (or any profit at all,) we would indeed be excited by all the business of game marketing and encouraged to tell you that everything is good. But no, we are the most jaded (and the poorest) people in the world, and so without further ado here is some very unappealing news about one of the specific ways corporations compete for your money: data mining!

I think this press release speaks for itself, so I’m going to let it do exactly that. Tip: just read until you feel nauseated.

“Mark Robinson Joins GamesAnalytics
Award Winning Data Analyst Joins Firm to Maximize Online Game Revenue

San Francisco, Calif. and Edinburgh, Scotland – May 23, 2011
Award winning analytics expert Mark Robinson has joined data mining and monetization company GamesAnalytics as Chief Operating Officer.

Robinson has worked within various sectors of the data mining industry his entire career, initially as an analyst at British Gas then as a consultant with leading marketing services company CACI.

Prior to joining GamesAnalytics, Robinson led the data-mining consultancy Marketing Databasics, a company that builds and hosts analytical environments to increase customer value through data-driven insight. His experience covers a wide range of sectors including financial services, retail, leisure and the drinks industry. Previous clients include AXA, Barclays, HBOS, Standard Life and Sainsbury’s Finance. In 2009, Marketing Databasics was designated a ‘Leader’ organization in the Forrester review of marketing services providers for its completeness of offering and its strategic vision. In addition to having received several industry awards for innovative use of data, Robinson contributes to thought leadership in the industry through his membership on the Direct Marketing Association Council.

Chris Wright, GamesAnalytics CEO said:
“Mark brings unparalleled experience in data-mining and analytics, having built market leading analytics solutions for blue chip clients throughout the financial and retail sectors. Mark’s understanding of driving customer satisfaction and monetization for clients through data driven insight is second to none and we are thrilled to have him join the team.”

Commenting on his appointment Robinson said:
“I’m tremendously excited to have joined GamesAnalytics. The rise in online gaming presents a massive opportunity for developers and publishers to understand their customers as never before. It is now possible to use state-of-the-art behavioral analytics technology to drive increased customer satisfaction, player retention, virality and revenue. I look forward to meeting with gaming companies and explaining GamesAnalytics unique vision over the coming months.”

About GamesAnalytics
GamesAnalytics is a data mining and monetization company serving the online videogames industry. The company maximizes online game revenue with state-of-the-art real-time behavioral analytics and individually targeted in-game marketing. Its products, coupled with a deep knowledge and passion for the games industry will increase the number of paying players, ARPU and player satisfaction and retention.

GamesAnalytics was founded in 2010 to bring the sophisticated analytics and monetization tools of the financial sector to the games industry. GamesAnalytics unlocks the true value of player data. The company has offices in Edinburgh, London, and San Francisco.”

Aren’t you just so excited about your data being mined!? Let’s have a look at the GamesAnalytics website to see exactly what the company does.

“We will often look at bespoke data mining, delving deep into your play data to really understand how players interact with the game and find what is stopping players from spending more money. Techniques like predictive analysis help us to identify key player behaviour patterns and identify potential revenue opportunities. “

Wow, that’s awesome. I wasn’t feeling quite enough like a paranoid schizophrenic, so it’s great to hear that my every move in a game is being analyzed by someone whose job it is to determine how to make me more profitable. It’s especially comforting to see clearly from their website that the company displays such attention to detail.

The existence of a company like this strikes a similar chord with me as Activision’s announcement that Modern Warfare 3 will include an optional subscription service. The PR paints a very rosy picture of the service, but do they actually think that gamers will react positively to the idea? Of course not. Will they make more money this way? Probably.

Of course, every major industry acts this way. Thor was a pretty good movie mostly because it’s easier to sell tickets to pretty good movies. But your transaction with Paramount Pictures ended as soon as you bought your ticket. The games industry is able to be more invasive. Their product becomes a part of your digital domain, able to place spy cameras where you’ll never notice them. Achievements, a common feature in modern games, are one example: they were invented to allow companies to collect data on player habits. Let’s have a look at one of those EULAs you never bother to read, this one from Rockstar Games:

“When using products or services on internet-capable hardware, the Company may receive information regarding your gameplay without any additional notice to you or actions taken by you. The Company will not receive personal information such as your name and address but may receive other information such as your PlayStation ID, Games for Windows LIVE or Xbox Live ID, game achievements, game scores and performance, Internet Protocol (IP) address, other console use information, or other information and statistics regarding your usage of the games. Information about gameplay may be collected while you are offline and transmitted to the Company when you next connect to the Internet whether or not you are currently logged into your Internet Connection from your console, handheld, computer, or other gaming platform. The Company may use such information as set forth in this Privacy Policy whether or not you register for or use the Online Services.”

Valve won a lot of hearts with their ‘game as a service’ philosophy that led to so many free addons for Team Fortress 2. I even admit that I would love to pay Valve a monthly fee if it would fund a regular stream of Portal 2 add-ons. But that doesn’t mean that Valve do it out of love for the customer, and it’s worth being wary of any company’s market research tactics when you are letting them into your digital life.


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