Tadeusz Morzy

Creation and management of versions in multiversion data warehouse

A data warehouse (DW) provides an information for analytical processing, decision making, and data mining tools. On the one hand, the structure and content of a data warehouse reflects a real world, i.e. data stored in a DW come from real production systems. On the other hand, a DW and its tools may be used for predicting trends and simulating a virtual business scenarios. This activity is often called the what-if analysis. Traditional DW systems have static structure of their schemas and relationships between data, and therefore they are not able to support any dynamics in their structure and content. For these purposes, multiversion data warehouses seem to be very promising. In this paper we present a concept and an ongoing implementation of a multiversion data warehouse that is capable of handling changes in the structure of its schema as well as simulating alternative business scenarios.

On querying versions of multiversion data warehouse

A data warehouse (DW) is fed with data that come from external data sources that are production systems. External data sources, which are usually autonomous, often change not only their content but also their structure. The evolution of external data sources has to be reflected in a DW, that uses the sources. Traditional DW systems offer a limited support for handling dynamics in their structure and content. A promising approach to handling changes in DW structure and content is based on a multiversion data warehouse. In such a DW, each DW version describes a schema and data at certain period of time or a given business scenario, created for simulation purposes. In order to appropriately analyze multiversion data, an extension to a traditional SQL language is required. In this paper we propose an approach to querying a multiversion DW. To this end, we extended a SQL language and built a multiversion query language interface with functionality that allows: (1) expressing queries that address several DW versions and (2) presenting their results annotated with metadata information.

Materialized Data Mining Views

Data mining is a useful decision support technique, which can be used to find trends and regularities in warehouses of corporate data. A serious problem of its practical applications is long processing time required by data mining algorithms. Current systems consume minutes or hours to answer simple queries. In this paper we present the concept of materialized data mining views. Materialized data mining views store selected patterns discovered in a portion of a database, and are used for query rewriting, which transforms a data mining query into a query accessing a materialized view. Since the transformation is transparent to a user, materialized data mining views can be created and used like indexes.

Scalable Hierarchical Clustering Method for Sequences of Categorical Values

Data clustering methods have many applications in the area of data mining. Traditional clustering algorithms deal with quantitative or categorical data points. However, there exist many important databases that store categorical data sequences, where significant knowledge is hidden behind sequential dependencies between the data. In this paper we introduce a problem of clustering categorical data sequences and present an efficient scalable algorithm to solve the problem. Our algorithm implements the general idea of agglomerative hierarchical clustering and uses frequently occurring subsequences as features describing data sequences. The algorithm not only discovers a set of high quality clusters containing similar data sequences but also provides descriptions of the discovered clusters.

Hierarchical Bitmap Index: An Efficient and Scalable Indexing Technique for Set-Valued Attributes

Set-valued attributes are convenient to model complex objects occurring in the real world. Currently available database systems support the storage of set-valued attributes in relational tables but contain no primitives to query them efficiently. Queries involving set-valued attributes either perform full scans of the source data or make multiple passes over single-value indexes to reduce the number of retrieved tuples. Existing techniques for indexing set-valued attributes (e.g., inverted files, signature indexes or RD-trees) are not efficient enough to support fast access of set-valued data in very large databases.

Handbook on Data Management in Information Systems

1. Management of Data: State-of-the-Art and Emerging Trends.- 1 Introduction.- 2 Survey of the Volume.- 2. Database Systems: from File Systems to Modern Database Systems.- 1 Introduction - Database Concepts.- 2 Database System Generations.- 3 Network Database Systems.- 4 Hierarchical Database Systems.- 5 Relational Database Systems.- 6 Object-Oriented Database Systems.- 7 Federated, Mediated Database Systems and Data Warehouses.- 8 Conclusions.- 3. Data Modeling.- 1 Introduction.- 2 Early Concerns in Data Management.- 3 Abstraction in Data Modeling.- 4 Semantic Data Models.- 5 Models of Reality and Perception.- 6 Toward Cognition-Based Data Management.- 7 A Cognitive Approach to Data Modeling.- 8 Research Directions.- 4. Object-Oriented Database Systems.- 1 Introduction and Motivation.- 2 Object-Oriented Data Modeling.- 3 The Query Language OQL.- 4 Physical Object Management.- 5 Architecture of Client-Server-Systems.- 6 Indexing.- 7 Dealing with Set-Valued Attributes.- 8 Query Optimization.- 9 Conclusion.- 5. High Performance Parallel Database Management Systems.- 1 Introduction.- 2 Partitioning Strategies.- 3 Join Using Inter-Operator Parallelism.- 4 ORE: a Framework for Data Migration.- 5 Conclusions and Future Research Directions.- 6. Advanced Database Systems.- 1 Introduction.- 2 Preliminaries.- 3 Data Models and Modeling for Complex Objects.- 4 Advanced Query Languages.- 5 Advanced Database Server Capabilities.- 6 Conclusions and Outlook.- 7. Parallel and Distributed Multimedia Database Systems.- 1 Introduction.- 2 Media Fundamentals.- 3 MPEG as an Example of Media Compression.- 4 Organisation and Retrieval of Multimedia Data.- 5 Data Models for Multimedia Data.- 6 Multimedia Retrieval Sequence Using Images as an Example.- 7 Requirements for Multimedia Applications.- 8 Parallel and Distributed Processing of Multimedia Data.- 9 Parallel and Distributed Techniques for Multimedia Databases.- 10 Case Study: Cairo - Cluster Architecture for Image Retrieval and Organisation.- 11 Conclusions.- 8. Workflow Technology: the Support for Collaboration.- 1 Introduction.- 2 Application Scenario and Collaboration Requirements.- 3 Commercial Technologies Addressing Collaboration Requirements.- 4 Evaluation of Current Workflow Management Technology.- 5 Research Problems, Related Work, and Directions.- 6 Summary.- 9. Data Warehouses.- 1 Introduction.- 2 Basics.- 3 The Database of a Data Warehouse.- 4 The Data Warehouse Concept.- 5 Data Analysis of a Data Warehouse.- 6 Building a Data Warehouse.- 7 Future Research Directions.- 8 Conclusions.- 10. Mobile Computing.- 1 Introduction.- 2 Mobile Computing Infrastructure.- 3 Mobile Computing Software Architectures and Models.- 4 Disconnected Operation.- 5 Weak Connectivity.- 6 Data Delivery by Broadcast.- 7 Mobile Computing Resources and Pointers.- 8 Conclusions.- 11. Data Mining.- 1 Introduction.- 2 Mining Associations.- 3 Classification and Prediction.- 4 Clustering.- 5 Conclusions.- List of Contributors.

Pattern-Oriented Hierachical Clustering

Clustering is a data mining method, which consists in discovering interesting data distributions in very large databases. The applications of clustering cover customer segmentation, catalog design, store layout, stock market segmentation, etc. In this paper, we consider the problem of discovering similarity-based clusters in a large database of event sequences. We introduce a hierarchical algorithm that uses sequential patterns found in the database to efficiently generate both the clustering model and data clusters. The algorithm iteratively merges smaller, similar clusters into bigger ones until the requested number of clusters is reached. In the absence of a well-defined metric space, we propose the similarity measure, which is used in cluster merging. The advantage of the proposed measure is that no additional access to the source database is needed to evaluate the inter-cluster similarities.

Data Mining Support in Database Management Systems

The most popular data mining techniques consist in searching databases for frequently occurring patterns, e.g. association rules, sequential patterns. We argue that in contrast to todays loosely-coupled tools, data mining should be regarded as advanced database querying and supported by Database Management Systems (DBMSs). In this paper we descirbe our research prototype system, which logically extends DBMS functionality, offering extensive support for pattern discovery, storage and management. We focus on the system architecture and novel SQL-based data mining query language, which serves as the user interface to the system.

Managing and Querying Versions of Multiversion Data Warehouse

A data warehouse (DW) is a database that integrates external data sources (EDSs) for the purpose of advanced data analysis. The methods of designing a DW usually assume that a DW has a static schema and structures of dimensions. In practice, schema and dimensions’ structures often change as the result of the evolution of EDSs, changes of the real world represented in a DW, new user requirements, new versions of software being installed, and system tuning activities. Examples of various change scenarios can be found in [1,8].

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.