Data Mining Query Language Research Articles

A Study: Web Data Mining Challenges and Application for Information Extraction

Data mining has attracted a great deal of attention in the information industry and in society as a whole in recent years, due to the wide availability of huge amounts of data and the imminent need for turning such data into useful information and knowledge. The information and knowledge gained can be used for applications ranging from market analysis, fraud detection, and customer retention, to production control and various explorations. The aim of this paper is to explore the role of data mining for information extraction in web content, structure and usages mining in current web models, and the outlines the process of extracting patterns from data. This paper also present data mining primitives, from which data mining query languages can be designed. Issues regarding how to integrate a data mining system with a database or data warehouse are also discussed. In addition to studying a classification of data mining systems, and it's challenging research issues for building data mining tools of the future. Keywords - Data Mining, Information Extraction, Knowledge discovery, Web mining

An inductive database system based on virtual mining views

Inductive databases integrate database querying with database mining. In this article, we present an inductive database system that does not rely on a new data mining query language, but on plain SQL. We propose an intuitive and elegant framework based on virtual mining views, which are relational tables that virtually contain the complete output of data mining algorithms executed over a given data table. We show that several types of patterns and models that are implicitly present in the data, such as itemsets, association rules, and decision trees, can be represented and queried with SQL using a unifying framework. As a proof of concept, we illustrate a complete data mining scenario with SQL queries over the mining views, which is executed in our system.

A Query Language for Mobility Data Mining

The technologies of mobile communications and ubiquitous computing pervade society. Wireless networks sense the movement of people and vehicles, generating large volumes of mobility data, such as mobile phone call records and GPS tracks. This data can produce useful knowledge, supporting sustainable mobility and intelligent transportation systems, provided that a suitable knowledge discovery process is enacted for mining this mobility data. In this paper, the authors examine a formal framework, and the associated implementation, for a data mining query language for mobility data, created as a result of a European-wide research project called GeoPKDD (Geographic Privacy-Aware Knowledge Discovery and Delivery). The authors discuss how the system provides comprehensive support for the Mobility Knowledge Discovery process and illustrate its analytical power in unveiling the complexity of urban mobility in a large metropolitan area, based on a massive real life GPS dataset.

ST‐DMQL: A Semantic Trajectory Data Mining Query Language

Mobile devices are becoming very popular in recent years, and large amounts of trajectory data are generated by these devices. Trajectories left behind cars, humans, birds or other objects are a new kind of data which can be very useful in the decision making process in several application domains. These data, however, are normally available as sample points, and therefore have very little or no semantics. The analysis and knowledge extraction from trajectory sample points is very difficult from the user's point of view, and there is an emerging need for new data models, manipulation techniques, and tools to extract meaningful patterns from these data. In this paper we propose a new methodology for knowledge discovery from trajectories. We propose through a semantic trajectory data mining query language several functionalities to select, preprocess, and transform trajectory sample points into semantic trajectories at higher abstraction levels, in order to allow the user to extract meaningful, understandable, and useful patterns from trajectories. We claim that meaningful patterns can only be extracted from trajectories if the background geographical information is considered. Therefore we build the proposed methodology considering both moving object data and geographic information. The proposed language has been implemented in a toolkit in order to provide a first software prototype for trajectory knowledge discovery.

5th international workshop on knowledge discovery in inductive databases (KDID'06)

This report presents a review of the 5th International Workshop on Knowledge Discovery in Inductive Databases (KDID'06), which was organized by the authors and held in Berlin, Germany, on September 18, 2006, in conjunction with ECML/PKDD'06, the 17th European Conference on Machine Learning and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases. The goal of the workshop was to bring together the researchers that are interested in the area of inductive databases, inductive queries, constraint-based data mining, and data mining query languages.

ExAnte: A Preprocessing Method for Frequent-Pattern Mining

Our main research objective is to define a data mining query language, supported by a system that can optimize constraint-based data mining queries. We have invented ExAnte, a simple yet effective preprocessing technique for frequent-pattern mining. ExAnte exploits constraints to dramatically reduce the analyzed data to those containing patterns of interest. This data reduction, in turn, induces a strong reduction of the candidate patterns' search space, thus supporting substantial performance improvements in subsequent mining.

An XML-enabled data mining query language: XML-DMQL

Inspired by the good work of Han et al. (1996) and Elfeky et al. (2001) on the design of data mining query languages for relational and object-oriented databases, in this paper, we develop an expressive XML-enabled data mining query language by extension of XQuery. We first describe some preliminaries on the extension of traditional rule mining to XML mining. The philosophy that guides the language design is then elaborated. We detail the syntax of the mining language and its usage with a number of examples.