Zbyszko Królikowski

OLAP-Like analysis of time point-based sequential data

Nowadays business intelligence technologies allow to analyze mainly set oriented data, without considering order dependencies between data. Few approaches to analyzing data of sequential order have been proposed so far. Nonetheless, for storing and manipulating sequential data the approaches use either the relational data model or its extensions. We argue that in order to be able to fully support the analysis of sequential data, a dedicated new data model is needed. In this paper, we propose a formal model for time point-based sequential data with operations that allow to construct sequences of events, organize them in an OLAP-like manner, and analyze them. To the best of our knowledge, this is the first formal model and query language for this class of data.

Managing evolution of data warehouses by means of nested transactions

In this paper we address problems of managing data warehouses (DWs) that evolve in time and we demonstrate that transactional maintenance of evolving DWs is inevitable. To this end, we propose a nested transaction model. In this model we define 5 types of transactions each of which is responsible for certain tasks. The tasks and properties of these transactions are characterized in the paper.

Query optimization in multidatabase systems: solutions and open issues

Query optimization in multidatabase systems differs from that of distributed homogeneous database systems due to the lack of information about cost formulae of component local database systems as well as due to less control over local query execution. The paper provides an overview of query processing in multidatabase systems. It includes the architecture of a multidatabase system, query execution model and multidatabase query optimization. Some results are surveyed and some open issues are discussed.

Incremental association rule mining using materialized data mining views

Data mining is an interactive and iterative process. Users issue series of similar queries until they receive satisfying results, yet currently available data mining systems do not support iterative processing of data mining queries and do not allow to re-use the results of previous queries. Consequently, mining algorithms suffer from long processing times, which are unacceptable from the point of view of interactive data mining. On the other hand, the results of consecutive data mining queries are usually very similar. This observation leads to the idea of reusing materialized results of previous data mining queries. We present the notion of a materialized data mining view and we propose two novel algorithms which aim at efficient discovery of association rules in the presence of materialized results of previous data mining queries.

Comparison of Genetic and Tabu Search Algorithms in Multiquery Optimization in Advanced Database Systems

In several database applications sets of related queries are submitted together to be processed as a single unit. In all these cases the queries usually have some degree of overlap, i.e. may have common subqueries. Therefore a significant performance improvement can be obtained by optimizing and executing the entire group of queries as a whole, thus avoiding to duplicate the optimization and processing effort for common parts. This has suggested an approach, termed multiquery optimization (MQO) that has been proposed and studied by several authors. In this paper we suggest a new approach to multiple-query optimization based on Genetic and Tabu Search algorithms that ensure the tractability of the problem even for very large size of the queries. To analyze the performance of the algorithms, we have run a set of experiments that allow to understand how the different approaches are sensitive to the main workload parameters.

Database Systems: from File Systems to Modern Database Systems

Database systems have evolved from simple record-oriented navigational database systems, hierarchical and network systems, into set-oriented systems that gave way to relational database systems. The relational database systems are now evolving into object-relational and multimedia database systems. During the last years, database systems have been used as platforms for managing data for conventional transaction-oriented business applications. As organizations and companies have become more sophisticated, pressure to provide data integration across different, heterogeneous data sources has mounted. The problem how to provide organization-wide uniform access to heterogeneous and autonomous external data sources (file systems, databases, Web pages, etc.) can be addressed, genrally, in three different ways: federated databases, data warehousing systems, and mediated systems.

Other comments on 'Optimization algorithms for distributed queries' by P.M.G. Apears

An erroneous fact concerning the assumption of irreducibility of nonjoining attributes of the distributed query optimization algorithm called GENERAL presented in the above paper (see ibid., vol.SE-9, no.1, p.57-68, Jan. 1983) is pointed out. It is shown that it is possible to generate an efficient semijoin program with better response time than the one produced by the GENERAL algorithm. A counterexample that proves this possibility is provided. >