Giuseppe Psaila

An Extension to SQL for Mining Association Rules

Data mining evolved as a collection of applicative problems and efficient solution algorithms relative to rather peculiar problems, all focused on the discovery of relevant information hidden in databases of huge dimensions. In particular, one of the most investigated topics is the discovery of association rules.This work proposes a unifying model that enables a uniform description of the problem of discovering association rules. The model provides a SQL-like operator, named X⇒Y, which is capable of expressing all the problems presented so far in the literature concerning the mining of association rules. We demonstrate the expressive power of the new operator by means of several examples, some of which are classical, while some others are fully original and correspond to novel and unusual applications. We also present the operational semantics of the operator by means of an extended relational algebra.

Designing Templates for Mining Association Rules

Current approaches to data mining usually address specific userrequests, while no general design criteria for the extraction of associationrules are available for the end-user. In this paper, we propose aclassification of association rule types, which provides a general frameworkfor the design of association rule mining applications. Based on theidentified association rule types, we introduce predefined templates as ameans to capture the user specification of mining applications. Furthermore,we propose a general language to design templates for the extraction ofarbitrary association rule types.

Incremental Refinement of Mining Queries

A first attempt to extract association rules from a database frequently yields a significant number of rules, which may be rather difficult for the user to browse in searching interesting information. However, powerful languages allow the user to specify complex mining queries to reduce the amount of extracted information. Hence, a suitable rule set may be obtained by means of a progressive refinement of the initial query. To assist the user in the refinement process, we identify several types of containment relationships between mining queries that may lead the process. Since the repeated extraction of a large rule set is computationally expensive, we propose an algorithm to perform an incremental recomputation of the output rule set. This algorithm is based on the detection of containment relationships between mining queries.

ERX: a conceptual model for XML documents

The eXtensible Markup Language (XML) is able to represent any kind of structured or semi-structured document, such as papers, web pages, database schemas and instances, style-sheets, etc.. However, the tree-structure of XML documents , induced by nested markups , does not provide a suuciently expressive and general conceptual model of data in the documents, particularly when multiple source documents are processed at the same time. This paper proposes the ERX (Entity Relationship for XML) conceptual model, an evolution of the Entity Relationship model that copes with the peculiar features of XML. ERX is devised to provide an eeective support to the development of complex XML processors for advanced applications. By discussing an applicative scenario, the paper shows that suitable CASE tools can provide a practical support during the implementation of XML processors, by the automatic generation of software components based on the ERX model.

A tightly-coupled architecture for data mining

Current approaches to data mining are based on the use of a decoupled architecture, where data are first extracted from a database and then processed by a specialized data mining engine. This paper proposes instead a tightly-coupled architecture, where data mining is integrated within a classical SQL server. The premise of this work is a SQL-like operator, called MINE RULE. We show how the various syntactic features of the operator can be managed by either a SQL engine or a classical data mining engine; our main objective is to identify the border between typical relational processing, executed by the relational server, and data mining processing, executed by a specialized component. The resulting architecture exhibits portability at the SQL level and integration of inputs and outputs of the data mining operator with the database, and provides the guidelines for promoting the integration of other data mining techniques and systems with SQL servers.

Composite Events in Chimera

In this paper, we extend event types supported by Chimera, an active object-oriented database system. Chimera rules currently support disjunctive expressions of set-oriented, elementary event types; our proposal introduces instance-oriented event types, arbitrary boolean expressions (including negation), and precedence operators. Thus, we introduce a new event calculus, whose distinguishing feature is to support a minimal set of orthogonal operators which can be arbitrarily composed. We use event calculus to determine when rules are triggered; this is a change of each rules internal status which makes it suitable for being considered by the rule selection mechanism.

Hierarchy-based mining of association rules in data warehouses

The problem of mining association rules from large databases is knowing an increasing interest from the Data Mining community. Separately, data warehouses axe rapidly becoming a standard platform to collect and analyze huge amounts of data. Their main characteristic is that warehouses maintain the structural complexity of data through a suitable data schema which can represent explicit/implicit concept hierarchies over the dimensions of data. The data mining tools for extracting association rules rarely exploit such hierarchies since they usually consider a flat representation of data. In this paper, we introduce an analysis method to assist the process of extracting association rules by exploiting the implicit and explicit concepts hierarchies that feature data warehouses. This method is devised to automate as mush as possible the process of knowledge discovery on top of data warehouses in the specific case of association rules. Specifically, our method tries to suggest only association rule patterns that can extract potentially interesting knowledge from the data. The method we propose appears to be very general and does not depend on specific mining algorithm or tool•

Integrating trust management and access control in data-intensive Web applications

The widespread diffusion of Web-based services provided by public and private organizations emphasizes the need for a flexible solution for protecting the information accessible through Web applications. A promising approach is represented by credential-based access control and trust management. However, although much research has been done and several proposals exist, a clear obstacle to the realization of their benefits in data-intensive Web applications is represented by the lack of adequate support in the DBMSs. As a matter of fact, DBMSs are often responsible for the management of most of the information that is accessed using a Web browser or a Web service invocation. In this article, we aim at eliminating this gap, and present an approach integrating trust management with the access control of the DBMS. We propose a trust model with a SQL syntax and illustrate an algorithm for the efficient verification of a delegation path for certificates. Our solution nicely complements current trust management proposals allowing the efficient realization of the services of an advanced trust management model within current relational DBMSs. An important benefit of our approach lies in its potential for a robust end-to-end design of security for personal data in Web scenario, where vulnerabilities of Web applications cannot be used to violate the protection of the data residing on the database server. We also illustrate the implementation of our approach within an open-source DBMS discussing design choices and performance impact.

Toward XML-based knowledge discovery systems

Inductive databases are intended to be general purpose databases in which both source data and mined patterns can be represented, retrieved and manipulated. However, the heterogeneity of models for mined patterns makes difficult to realize them. In this paper, we explore the feasibility of using XML as the unifying framework for inductive databases, introducing a suitable data model called XDM (XML for data mining). XDM is designed to describe source raw data, heterogeneous mined patterns and data mining statements, so that they can be stored inside a unique XML-based inductive database.

Managing uncertainty in location-based queries

Location-based queries (LBQ) are becoming more and more useful in location-based services (LBSs) such as those provided through mobile phones, personal digital assistants (PDAs), and laptops. They are context aware since they support the access to information by taking into account the spatial context of the user when submitting the query, and the spatial location of the searched information (instances). Generally, the key-selection condition is a constraint on the distance of the instances from the user location. One deficiency of current approaches in evaluating LBQs is the fact that they do not manage the uncertainty that often characterizes the knowledge of either the user position or the searched instances or both of them, thus they do not produce query answers with estimates of their possible validity. In the paper, after analyzing the processes involved in a LBS that may generate uncertainty, a model for representing and evaluating LBQs affected by uncertainty is proposed, in which uncertainty and imprecision can affect both location information and the spatial condition, i.e., the query scope. Distinct situations of uncertainty in LBQs are analyzed and for each of them a two-step evaluation procedure is proposed based on a fixed-cost filter phase and on a refinement phase that produces ranked results reflecting an estimate of their validity.