The conventional star schema model of Data Warehouse (DW) has its limitations due to the nature of the relational data model. Firstly, this model cannot represent the semantics and operations of multi-dimensional data adequately. Due to the hidden semantics, it is difficult to efficiently address the problems of view design. Secondly, as we move up to higher levels of summary data (multiple complex aggregations), SQL queries do not portray the intuition needed to facilitate buildingand supporting efficient execution of complex queries on complex data. In light of these issues, we propose the Object-Relational View (ORV) design for DWs. Using Object-Oriented (O-O) methodology, we can explicitly represent the semantics and reuse view (class) definitions based on the ISA hierarchy and the class composition hierarchies, thereby resulting in a more efficient view mechanism. Part of the design involves providing a translation mechanism from the star/snowflake schema to an O-O representation. This is done by flattening the fact-dimension schema and converting it to a class-composition hierarchy in an O-O framework. Vertically partitioning this O-O schema further increases the efficiency of query execution by reducing disk access. We then build a Structural Join Index Hierarchy (SJIH) on this partitioned schema to facilitate complex object retrieval and avoid using a sequence of expensive pointer chasing (or join) operations.

Star/Snow-Flake Schema Driven Object-Relational Data Warehouse - Design and Query Processing Strategies

In order to support homogeneous distributed object oriented database systems we need to have a clear understanding of what is meant by partitioning a class and what are the different ways of doing so. In this paper, we concentrate on studying the different types of class partitioning schemes that can arise in object oriented databases. To come up with a general-purpose design methodology, we present a framework of devising partitioning schemes based on different types of methods and their classification. We also address the issue of “fragmentation transparency” by considering appropriate method transformation techniques. By laying down the foundation by articulating the concepts, representation, and implementation approaches for partitioning object databases, we facilitate further work on partitioning algorithms, query decomposition, optimization and transaction management for distributed OODBMS.

A Framework for Class Partitioning in Object-Oriented Databases

Index selection is one of the most important decisions to take in the physical design of relational data warehouses. Indices reduce significantly the cost of processing complex OLAP queries, but require storage cost and induce maintenance overhead. Two main types of indices are available: mono-attribute indices (e.g., B-tree, bitmap, hash, etc.) and multi-attribute indices (join indices, bitmap join indices). To optimize star join queries characterized by joins between a large fact table and multiple dimension tables and selections on dimension tables, bitmap join indices are well adapted. They require less storage cost due to their binary representation. However, selecting these indices is a difficult task due to the exponential number of candidate attributes to be indexed. Most of approaches for index selection follow two main steps: (1) pruning the search space (i.e., reducing the number of candidate attributes) and (2) selecting indices using the pruned search space. In this paper, we first propose a data mining driven approach to prune the search space of bitmap join index selection problem. As opposed to an existing our technique that only uses frequency of attributes in queries as a pruning metric, our technique uses not only frequencies, but also other parameters such as the size of dimension tables involved in the indexing process, size of each dimension tuple, and page size on disk. We then define a greedy algorithm to select bitmap join indices that minimize processing cost and verify storage constraint. Finally, in order to evaluate the efficiency of our approach, we compare it with some existing techniques.

A Data Mining Approach for Selecting Bitmap Join Indices

In this chapter, we present a basic introduction to two very important areas of research in the domain of Information Technology, namely, video data management and video information retrieval. Both of these areas need additional research efforts to seek solutions to many unresolved problems for efficient data management and information retrieval. We discuss those issues and relevant works done so far in these two fields. INTRODUCTION An enormous amount of video data is being generated these days all over the world. This requires efficient and effective mechanisms to store, access, and retrieve these data. But the technology developed to date to handle those issues is far from the level of maturity required. Video data, as we know, would contain image, audio, graphical and textual data. The first problem is the efficient organization of raw video data available from various sources. There has to be proper consistency in data in the sense that data are to be stored in a standard format for access and retrieval. Then comes the issue of compressing the data to reduce the storage space required, since the data could be really voluminous. Also, various features of video data have to be extracted from low-level features like shape, color, texture, and spatial relations and stored efficiently for access. 701 E. Chocolate Avenue, Suite 200, Hershey PA 17033-1240, USA Tel: 717/533-8845; Fax 717/533-8661; URL-http://www.irm-press.com IRM PRESS This chapter appears in the book, Video Data Management and Information Retrieval by Sagarmay Deb. Copyright © 2005, IRM Press, an imprint of Idea Group Inc. Copying or distributing in print or electronic forms without written permission of Idea Group Inc. is prohibited.

Video Data Management and Information Retrieval

With the substantial development of digital technologies in multimedia, network communication and user interfaces, we are seeing an increasing number of applications of these technologies, in particular in the entertainment domain. They include computer gaming, e-learning, high-definition and interactive TVs, and virtual environments. The development of these applications typically involves the integration of existing technologies as well as the development of new technologies. This Introduction summarizes latest interactive entertainment technologies and applications, and briefly highlights some potential research directions. It also introduces the seven papers that are accepted to the special issue. Hopefully, this will provide the readers some insights into future research topics in interactive entertainment technologies and applications.

Emerging Technologies and Applications on Interactive Entertainments

.In object-oriented databases (OODBs), a method encapsulated in a class typically accesses a few, but not all the instance variables defined in the class. It may thus be preferable to vertically partition the class for reducing irrelevant data (instance variables) accessed by the methods. Our prior work has shown that vertical class partitioning can result in a substantial decrease in the total number of disk accesses incurred for executing a set of applications, but coming up with an optimal vertical class partitioning scheme is a hard problem. In this paper, we present two algorithms for deriving optimal and near-optimal vertical class partitioning schemes. The cost-driven algorithm provides the optimal vertical class partitioning schemes by enumerating, exhaustively, all the schemes and calculating the number of disk accesses required to execute a given set of applications. For this, a cost model for executing a set of methods in an OODB system is developed. Since exhaustive enumeration is costly and only works for classes with a small number of instance variables, a hill-climbing heuristic algorithm (HCHA) is developed, which takes the solution provided by the affinity-based algorithm and improves it, thereby further reducing the total number of disk accesses incurred. We show that the HCHA algorithm provides a reasonable near-optimal vertical class partitioning scheme for executing a given set of applications.

Cost-driven vertical class partitioning for methods in object oriented databases

Vertical partitioning is a design technique for reducing the number of disk accesses to execute a given set of queries by minimizing the number of irrelevant instance variables accessed. This is accomplished by grouping the frequently accessed instance variables as vertical class fragments. The complexity of object-oriented database models due to subclass hierarchy and class composition hierarchy complicates the definition and representation of vertical partitioning of the classes, which makes the problem of vertical partitioning in OODBs very challenging. In this paper, we develop a comprehensive analytical cost model for processing of queries on vertically partitioned OODB classes. A set of analytical evaluation results is presented to show the effect of vertical partitioning, and to study the trade-off between the projection ratio versus selectivity factor vis-a-vis sequential versus index access. Furthermore, an empirical experimental prototype supporting vertical class partitioning has been implemented on a commercial OODB tool kit to validate our analytical cost model.

An evaluation of vertical class partitioning for query processing in object-oriented databases

Vertical partitioning is a proven database design technique which increases the eflciency of query execution by reducing the irrelevant data accesses. In this paper, we develop a cost model to study the effectiveness of vertical partitioning in OODBs, in terms of reducing the number of disk accesses for executing a set of queries. Further, we compare and evaluate two different (namely, afinity-based, and cost-driven) partitioning approaches. We show that the cost-driven approach is more general and more effective in reducing number of irrelevant disk accesses accessed than the aftin@based approach.

Cost-Driven Evaluation of Vertical Class Partitioning in Object-Oriented Databases

Vertical class partitioning and complex object retrieval in object oriented databases

In this chapter, a novel indexing method --- structural join index hierarchy --- is proposed to handle the complex object retrieval without using a sequence of expensive pointer chasing operations. The structural join index hierarchy mimics the class composition hierarchy of the complex objects and provides direct access to them and their component objects. Structural join index hierarchy extends the work on join index hierarchies by providing efficient and flexible access to complex objects. Moreover, this method unifies various previous indexing methods proposed for OODBs. We present performance evaluation to illustrate the utility of structural join index hierarchy over other indexing methods for complex object retrieval.

Chi-Wai Fung

Papers

Cost-driven vertical class partitioning for methods in object oriented databases

An evaluation of vertical class partitioning for query processing in object-oriented databases

Cost-Driven Evaluation of Vertical Class Partitioning in Object-Oriented Databases

Vertical class partitioning and complex object retrieval in object oriented databases

A mechanism of structural join index hierarchy for efficient complex object retrieval