Evolution of data mining and warehousing

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Evolution of data mining and warehousing

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 the database community. At the beginning of the century, there was a phrase “database mining”™, trademarked by HNC, a San Diego-based company (now merged into FICO), to pitch their Data Mining Workstation; 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 (1989) and this term became more popular in AI and Machine Learning Community. However, the term data mining became more popular in the business and press communities. Currently, Data Mining and Knowledge Discovery are used interchangeably..


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, automated data processing, aided by other discoveries in computer science, such as neural networks, cluster analysis, genetic algorithms (1950s), decision trees (1960s), and support vector machines (1990s). Data mining is the process of applying these methods with the intention of uncovering hidden patterns 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 ever larger data sets.

Data mining is a natural development of the increased use of computerized databases to store data and provide answers to business analysts.

Evolutionary Step Business Question Enabling Technology
Data Collection (1960s) “What was my total revenue in the last five years?” computers, tapes, disks
Data Access (1980s) “What were unit sales in New England last March?” faster and cheaper computers with more storage, relational databases
Data Warehousing and Decision Support “What were unit sales in New England last March? Drill down to Boston.” faster and cheaper computers with more storage, On-line analytical processing (OLAP), multidimensional databases, data warehouses
Data Mining “What’s likely to happen to Boston unit sales next month? Why?” faster and cheaper computers with more storage, advanced computer algorithms

Traditional query and report tools have been used to describe and extract what is in a database. The user forms a hypothesis about a relationship and verifies it or discounts it with a series of queries against the data. For example, an analyst might hypothesize that people with low income and high debt are bad credit risks and query the database to verify or disprove this assumption. Data mining can be used to generate an hypothesis. For example, an analyst might use a neural net to discover a pattern that analysts did not think to try – for example, that people over 30 years old with low incomes and high debt but who own their own homes and have children are good credit risks.

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