Sangkyu Rho

Allocating data and operations to nodes in distributed database design

The allocation of data and operations to nodes in a computer communications network is a critical issue in distributed database design. An efficient distributed database design must trade off performance and cost among retrieval and update activities at the various nodes. It must consider the concurrency control mechanism used as well as capacity constraints at nodes and on links in the network. It must determine where data will be allocated, the degree of data replication, which copy of the data will be used for each retrieval activity, and where operations such as select, project, join, and union will be performed. We develop a comprehensive mathematical modeling approach for this problem. The approach first generates units of data (file fragments) to be allocated from a logical data model representation and a characterization of retrieval and update activities. Retrieval and update activities are then decomposed into relational operations on these fragments. Both fragments and operations on them are then allocated to nodes using a mathematical modeling approach. The mathematical model considers network communication, local processing, and data storage costs. A genetic algorithm is developed to solve this mathematical formulation. >

Evaluating the efficiency of a two‐stage production process using data envelopment analysis

Data envelopment analysis (DEA) has been widely used to evaluate the comparative efficiencies of production processes. Most of the DEA applications assume that production processes consist of one stage. However, many production processes such as IT investments have more than one stage. In a two-stage production process, the first stage inputs produce intermediate outputs, which are used as inputs to the second stage to produce the final outputs. In such cases, using single-stage DEA may result in inaccurate efficiency evaluation. To address such problems, DEA models assuming two-stage production processes have been developed. In this paper, we extend two-stage DEA models by considering input and output slacks. We apply our model to the data from the banking industry and compare the results with those of the previous two-stage DEA models. Our model can identify weakly efficient units of evaluation that could not be identified by the previous models.

Evaluating ontology extraction tools using a comprehensive evaluation framework

Ontologies are a key component of the Semantic Web; thus, they are widely used in various applications. However, most ontologies are still built manually, a time-consuming activity which requires many resources. Several tools such as ontology editing tools, ontology merging tools, and ontology extraction tools have therefore been proposed to speed up ontology development. To minimize building time, one promising solution is the automation of the ontology development process. Consequently, the need for an automatic ontology extraction tool has increased in the last two decades and many tools have been developed for this purpose. However, there is still no comprehensive framework for evaluating such tools. In this paper, we proposed a set of criteria for evaluating ontology extraction tools and carried out an evaluation experiment on four ontology extraction tools (i.e., OntoLT, Text2Onto, OntoBuilder, and DODDLE-OWL) using our proposed evaluation framework. Based on the results of our experiment, we concluded that ontology extraction tools still lack the ability to automate the extraction process fully and thus require functional performance improvement.

Optimizing distributed join queries: A genetic algorithm approach

Abstractptimizing join queries is a major problem in distributed database systems, particularly when files are replicated and copies stored at different nodes in the network. A distributed query optimization algorithm must select file copies and determine how and where those files will be processed. Process decisions include which files to reduce via semijoins, if any, the sites at which to perform join operations, and the order in which to perform those join operations. We extend the scope of distributed query optimization research by develop-ing a model that, for the first time, includes all of these design decisions and considers both communication and local processing costs. We develop a genetic algorithm-based solution procedure for this model which quickly determines efficient query processing plans. We demonstrate that ignoring local processing costs or restricting join processing to the result site, as commonly done in prior research, can result in inefficient query execution plans.

A Link-Based Ranking Algorithm for Semantic Web Resources: A Class-Oriented Approach Independent of Link Direction

The information space of the Semantic Web has different characteristics from that of the World Wide Web WWW. One main difference is that in the Semantic Web, the direction of Resource Description Framework RDF links does not have the same meaning as the direction of hyperlinks in the WWW, because the link direction is determined not by a voting process but by a specific schema in the Semantic Web. Considering this fundamental difference, the authors propose a method for ranking Semantic Web resources independent of link directions and show the convergence of the algorithm and experimental results. This method focuses on the classes rather than the properties. The property weights are assigned depending on the relative significance of the property to the resource importance of each class. It solves some problems reported in prior studies, including the Tightly Knit Community TKC effect, as well as having higher accuracy and validity compared to existing methods.

A semantic object-oriented data access system

Abstract Lack of support for Entity-Relationship (E-R) semantics and the disconnect between object-oriented programming languages (OOPLs) and database languages remain key roadblocks to the effective use of object-orientation in information systems development. We present SOODAS, a Semantic Object-Oriented Data Access System that defines and manages the meta-data necessary to support E-R semantics and set level querying and provides related interface generation tools. SOODAS consists of five meta-classes. DomainObject and Relationship provide the capabilities needed to define and manage entities, attributes, relationships, external identifiers, and constraints. Together with the meta-class QueryNode , DomainObject provides an object-oriented, multi-entity querying capability. Queries can be arbitrarily complex and can include cycles and transitive closure. Persistence is provided by the meta-class, PermanentObject , of which DomainObject and Relationship are subclasses. The meta-class, DomainObjectInterface uses the meta-data in DomainObject and Relationship to generate a standard, re-usable interface for displaying and maintaining instances of any entity. Since SOODAS is implemented entirely in Smalltalk, it can be seamlessly integrated with any Smalltalk application.

A nested genetic algorithm for distributed database design

Distributed database design is a difficult and complex process involving two major, interrelated problems. First, data must be allocated to nodes in the network. Second given such an allocation data must be efficiently accessed, processed, and possibly communicated to meet the retrieval and update requirements of the users. Both of these problems can be formulated as constrained, integer, optimization problems; both of which are NP-hard. Genetic algorithms provide an efficient search method for problems of this type. We present a nested genetic algorithm that iteratively allocates data to nodes and determines where to perform access and processing operations to efficiently meet a specified set of retrieval and update requirements.<<ETX>>

Evaluation framework for automatic ontology extraction tools: an experiment

Ontologies have become increasingly important in many areas. Building ontology, however, is a time-consuming activity which requires many resources. Consequently, the need for the automatic ontology extraction tool has been increased for the last two decades, and many tools have been developed for this purpose. Yet, there is no comprehensive framework for evaluating such tools. In this paper, we identified important tool evaluation metrics and developed a set of criteria that guide us to evaluate the quality of ontology extraction tools. We carried out experiments and assessed four popular extraction tools using our proposed evaluation framework. The proposed framework can be applied as a useful benchmark when developers want to build ontology extraction tools.