Inductive Databases Research Articles

Inductive database to support iterative data mining: Application to biomarker analysis on patient data in the Fight-HF project

Machine learning is now an essential part of any biomedical study but its integration into real effective Learning Health Systems, including the whole process of Knowledge Discovery from Data (KDD), is not yet realised. We propose an original extension of the KDD process model that involves an inductive database. We designed for the first time a generic model of Inductive Clinical DataBase (ICDB) aimed at hosting both patient data and learned models. We report experiments conducted on patient data in the frame of a project dedicated to fight heart failure. The results show how the ICDB approach allows to identify biomarker combinations, specific and predictive of heart fibrosis phenotype, that put forward hypotheses relative to underlying mechanisms. Two main scenarios were considered, a local-to-global KDD scenario and a trans-cohort alignment scenario. This promising proof of concept enables us to draw the contours of a next-generation Knowledge Discovery Environment (KDE).

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.

Inductive database languages: requirements and examples

Inductive databases (IDBs) represent a database perspective on Knowledge discovery in databases (KDD). In an IDB, the KDD application can express both queries capable of accessing and manipulating data, and queries capable of generating, manipulating, and applying patterns allowing to formalize the notion of mining process. The feature that makes them different from other data mining applications is exactly the idea of looking at the support for knowledge discovery as an extension of the query process. This paper draws a list of desirable properties to be taken into account in the definition of an IDB framework. They involve several dimensions, such as the expressiveness of the language in representing data and models, the closure principle, the capability to provide a support for an efficient algorithm programming. These requirements are a basis for a comparative study that highlights strengths and weaknesses of existing IDB approaches. The paper focuses on the SQL-based ATLaS language/system, on the logic-based $${\mathcal{LDL}++}$$ language/system, and on the XML-based KDDML language/system.

A constraint-based querying system for exploratory pattern discovery

In this article we present ConQueSt, a constraint-based querying system able to support the intrinsically exploratory (i.e., human-guided, interactive and iterative) nature of pattern discovery. Following the inductive database vision, our framework provides users with an expressive constraint-based query language, which allows the discovery process to be effectively driven toward potentially interesting patterns. Such constraints are also exploited to reduce the cost of pattern mining computation. ConQueSt is a comprehensive mining system that can access real-world relational databases from which to extract data. Through the interaction with a friendly graphical user interface (GUI), the user can define complex mining queries by means of few clicks. After a pre-processing step, mining queries are answered by an efficient and robust pattern mining engine which entails the state-of-the-art of data and search space reduction techniques. Resulting patterns are then presented to the user in a pattern browsing window, and possibly stored back in the underlying database as relations.

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.

Integration of Structure−Activity Relationship and Artificial Intelligence Systems To Improve in Silico Prediction of Ames Test Mutagenicity

The Ames mutagenicity test in Salmonella typhimurium is a bacterial short-term in vitro assay aimed at detecting the mutagenicity caused by chemicals. Mutagenicity is considered as an early alert for carcinogenicity. After a number of decades, several (Q)SAR studies on this endpoint yielded enough evidence to make feasible the construction of reliable computational models for prediction of mutagenicity from the molecular structure of chemicals. In this study, we propose a combination of a fragment-based SAR model and an inductive database. The hybrid system was developed using a collection of 4337 chemicals (2401 mutagens and 1936 nonmutagens) and tested using 753 independent compounds (437 mutagens and 316 nonmutagens). The overall error of this system on the external test set compounds is 15% (sensitivity = 15%, specificity = 15%), which is quantitatively similar to the experimental error of Ames test data (average interlaboratory reproducibility determined by the National Toxicology Program). Moreover, each single prediction is provided with a specific confidence level. The results obtained give confidence that this system can be applied to support early and rapid evaluation of the level of mutagenicity concern.

Answering constraint-based mining queries on itemsets using previous materialized results

In recent years, researchers have begun to study inductive databases, a new generation of databases for leveraging decision support applications. In this context, the user interacts with the DBMS using advanced, constraint-based languages for data mining where constraints have been specifically introduced to increase the relevance of the results and, at the same time, to reduce its volume. In this paper we study the problem of mining frequent itemsets using an inductive database. We propose a technique for query answering which consists in rewriting the query in terms of union and intersection of the result sets of other queries, previously executed and materialized. Unfortunately, the exploitation of past queries is not always applicable. We then present sufficient conditions for the optimization to apply and show that these conditions are strictly connected with the presence of functional dependencies between the attributes involved in the queries. We show some experiments on an initial prototype of an optimizer which demonstrates that this approach to query answering is viable and in many practical cases it drastically reduces the query execution time.

A perspective on inductive databases

Inductive databases tightly integrate databases with data mining. The key ideas are that data and patterns (or models) are handled in the same way and that an inductive query language allows the user to query and manipulate the patterns (or models) of interest.This paper proposes a simple and abstract model for inductive databases. We describe the basic formalism, a simple but fairly powerful inductive query language, some basics of reasoning for query optimization, and discuss some memory organization and implementation issues.

SPADA: A Spatial Association Discovery System*

This paper presents a spatial association discovery system, named SPADA, which has been developed according to the theoretical framework of inductive databases. Our approach considers inductive databases as deductive databases with an integrated inductive component and relies on techniques borrowed from the field of Inductive Logic Programming (ILP). In SPADA, an ILP module supports the processing of spatial association mining queries and accesses spatial databases via a middle-layer module for feature extraction. The resulting architecture is illustrated by commenting spatial association mining sessions in detail and showing that ILP is a viable and promising solution to the development of inductive databases.

Building Knowledge Scouts Using KGL Metalanguage

Knowledge scouts are software agents that autonomously synthesize user-oriented knowledge (target knowledge) from information present in local or distributed databases. A knowledge generation metalanguage, KGL, is used to creating scripts defining such knowledge scouts. Knowledge scouts operate in an inductive database, by which we mean a database system in which conventional data and knowledge management operators are integrated with a wide range of data mining and inductive inference operators. Discovered knowledge is represented in two forms: (1) attributional rules, which are rules in attributional calculus—a logic-based language between prepositional and predicate calculus, and (2) association graphs, which graphically and abstractly represent relations expressed by the rules. These graphs can depict multi-argument relationships among different concepts, with a visual indication of the relative strength of each dependency. Presented ideas are illustrated by two simple knowledge scouts, one that seeks relations among lifestyles, environmental conditions, symptoms and diseases in a large medical database, and another that searches for patterns of children's behavior in the National Youth Survey database. The preliminary results indicate a high potential utility of this methodology for deriving knowledge from databases.

Inductive database relations

The concept of an inductive relation is introduced, as a natural development of other forms of intentional information, such as views and relations defined deductively. A class of top-down methods for computing such inductive relations is analyzed. Major problems produced by recursive and interdependent relations are considered. >

Induction in database systems: A bibliography

The integration of a conventional database system with the inductive inference mechanism has created a new type of system called an inductive database which has high potentials in many application areas. This paper provides an overview of several specific topics related to inductive databases and presents a bibliography for further reading in this area of research.

Precision teaching's unique legacy from B. F. Skinner

Precision Teaching's unique legacy from B. F. Skinner was the monitoring system of rate of response and the cumulative response recorder. This legacy is unique because the other instructional systems derived from Skinner's work do not use his monitoring method exclusively. Rate of response, cumulative recording and their extension to Precision Teaching's standard celeration charting are briefly described. In addition, Precision Teaching's nature, history, costs, distribution, inductive data-base, and academic base are briefly described. Skinner's legacy to education was a sound behavioral scientific base and his unique legacy to Precision Teaching was self-monitoring for real time decision making by learners and teachers.