Database and Expert Systems Applications 

About: Database and Expert Systems Applications is an academic conference. The conference publishes majorly in the area(s): XML & Relational database. Over the lifetime, 5171 publications have been published by the conference receiving 49618 citations.

Keys with Upward Wildcards for XML

Abstract: The paper proposes a key constraint language for XML and investigates its associated decision problems. The language is defined in terms of regular path expressions extended with downward and upward wildcards, which can not only move down XML document trees, but also upwards. In a uniform syntax it is capable of expressing both absolute keys and relative keys, which are important to hierarchically structured data. In addition, keys defined in the language can be reasoned about efficiently. The paper provides a sound and complete set of inference rules and a cubic time algorithm for determining implication of the keys.

The INQUERY Retrieval System

Abstract: As larger and more heterogeneous text databases become available, information retrieval research will depend on the development of powerful, efficient and flexible retrieval engines. In this paper, we describe a retrieval system (INQUERY) that is based on a probabilistic retrieval model and provides support for sophisticated indexing and complex query formulation. INQUERY has been used successfully with databases containing nearly 400,000 documents.

Stakeholder identification in the requirements engineering process

Abstract: Adequate, timely and effective consultation of relevant stakeholders is of paramount importance in the requirements engineering process. However, the thorny issue of making sure that all relevant stakeholders are consulted has received less attention than other areas which depend on it, such as scenario-based requirements, involving users in development, negotiating between different viewpoints and so on. The literature suggests examples of stakeholders, and categories of stakeholder, but does not provide help in identifying stakeholders for a specific system. In this paper, we discuss current work in stakeholder identification, propose an approach to identifying relevant stakeholders for a specific system, and propose future directions for the work.

Towards accountable management of identity and privacy: sticky policies and enforceable tracing services

Abstract: Digital identities and profiles are precious assets. On one hand they enable users to engage in transactions and interactions on the Internet. On the other hand, abuses and leakages of this information could violate the privacy of their owners, sometimes with serious consequences. Nowadays most of the people have limited understanding of security and privacy policies when applied to their confidential information and little control over the destiny of this information since it has been disclosed to third parties. In most cases this is a matter of trust. This document describes an innovative approach and related mechanisms to enforce users' privacy by putting users in control and making organizations more accountable. As part of our ongoing research activity, we introduce a technical solution based on sticky policies and tracing services that leverages identity-based encryption (IBE) and TCPA technologies. Work is in progress to build a full working prototype and deploy it in a real-life environment.

Cleansing Data for Mining and Warehousing

Abstract: Given the rapid growth of data, it is important to extract, mine and discover useful information from databases and data warehouses. The process of data cleansing is crucial because of the "garbage in, garbage out" principle. "Dirty" data files are prevalent because of incorrect or missing data values, inconsistent value naming conventions, and incomplete information. Hence, we may have multiple records refering to the same real world entity. In this paper, we examine the problem of detecting and removing duplicating records. We present several effcient techniques to pre-process the records before sorting them so that potentially matching records will be brought to a close neighbourhood. Based on these techniques, we implement a data cleansing system which can detect and remove more duplicate records than existing methods.