Myoung Ho Kim

Optimal file distribution for partial match retrieval

In this paper we present data distribution methods for parallel processing environment. The primary objective is to process partial match retrieval type queries for parallel devices. The main contribution of this paper is the development of a new approach called FX (Fieldwise eXclusive) distribution for maximizing data access concurrency. An algebraic property of exclusive-or operation, and field transformation techniques are fundamental to this data distribution techniques. We have shown through theorems and corollaries that this FX distribution approach performs better than other methods proposed earlier. We have also shown, by computing probability of optimal distribution and query response time, that FX distribution gives better performance than others over a large class of partial match queries. This approach presents a new basis in which optimal data distribution for more general type of queries can be formulated.

QEM: a scheduling method for wireless broadcast data

In mobile distributed systems the data on air can be accessed by a large number of clients. This paper describes the way clients access the wireless broadcast data with short latency. We define and analyze the problem of wireless data scheduling and we propose a measure, named QueryDistance (QD), which represents the degree of coherence for the data set accessed by a query. We give a practically usable method named QEM which constructs the broadcast schedule by expanding each querys data set in greedy way. We also evaluate the performance of our method by experiments.

Rewriting OLAP queries using materialized views and dimension hierarchies in data warehouses

OLAP queries involve a lot of aggregations on a large amount of data in data warehouses. To process expensive OLAP queries efficiently, we propose a new method for rewriting a given OLAP query using the various kinds of materialized aggregate views which already exist in data warehouses. We first define the normal forms of OLAP queries and materialized views based on the lattice of dimension hierarchies and the semantic information in data warehouses. Conditions for the usability of a materialized view in rewriting a given query are specified by relationships between the components of their normal forms. We present a rewriting algorithm for OLAP queries that effectively utilizes existing materialized views. The proposed algorithm can make use of materialized views having different selection granularities, selection regions and aggregation granularities together, to generate an efficient rewritten query.

Efficient incremental view maintenance in data warehouses

In the data warehouse environment, the concept of a materialized view is nowadays common and important in an objective of efficiently supporting OLAP query processing. Materialized views are generally derived from select-project-join of several base relations. These materialized views need to be updated when the base relations change. Since the propagation of updates to the views may impose a significant overhead, it is very important to update the warehouse views efficiently. Though various view maintenance strategies have been discussed so far, they typically require too much access to base relations, resulting in the performance degradation.In this paper we propose an efficient incremental view maintenance strategy called delta propagation that can minimize the total size of base relations accessed by analyzing the properties of base relations. We first define the delta expression and a delta propagation tree which are core concepts of the strategy. Then, a dynamic programming algorithm that can find the optimal delta expression are proposed. We also present various experimental results that show the usefulness and efficiency of the strategy.

Hierarchically organized skew-tolerant histograms for geographic data objects

Histograms have been widely used for fast estimation of query result sizes in query optimization. In this paper, we propose a new histogram method, called the Skew-Tolerant Histogram (STHistogram) for two or three dimensional geographic data objects that are used in many real-world applications in practice. The proposed method provides a significantly enhanced accuracy in a robust manner even for the data set that has a highly skewed distribution. Our method detects hotspots present in various parts of a data set and exploits them in organizing histogram buckets. For this purpose, we first define the concept of a hotspot, and provide an algorithm that efficiently extracts hotspots from the given data set. Then, we present our histogram construction method that utilizes hotspot information. We also describe how to estimate query result sizes by using the proposed histogram. We show through extensive performance experiments that the proposed method provides better performance than other existing methods.

An accurate lecture video segmentation method by using sift and adaptive threshold

Much research has been done in the past for segmenting lecture videos by detecting slide transitions. However, they do not perform well on certain kinds of videos recorded under non-stationary settings: the changes of a camera position or focus during a lecture. Since such non-stationary settings greatly affect visual properties of slides, the existing approaches utilizing global features and a global threshold, often have trouble in computing similarities between slides. In this paper, we propose a highly accurate method for lecture video segmentation by using SIFT and an adaptive threshold. By using SIFT, we can reliably match two slides whose contents are the same but are visually different. We also propose an adaptive threshold selection algorithm that detects slide transitions accurately by considering characteristics of features. Through various experiments that use real lecture videos, we show that our method provides 30% improvement in the average F1-score over other existing methods.

On effective data clustering in bitemporal databases

Temporal databases provide built-in supports for efficient recording and querying of time-evolving data. In this paper, data clustering issues in temporal database environment are addressed. Data clustering is one of the most effective techniques that can improve performance of a database system. However, data clustering methods for conventional databases do not perform well in temporal databases because there exist crucial differences between their query patterns. We propose a data clustering measure, called Temporal Affinity, that can be used for the clustering of temporal data. The temporal affinity, which is based on the analysis of query patterns in temporal databases, reflects the closeness of temporal data objects in viewpoints of temporal query processing. We perform experiments to evaluate the proposed measure. The experimental results show that a data clustering method with the temporal affinity works better than other methods.

Processing Top-k Monitoring Queries in Wireless Sensor Networks

Top-k monitoring queries are useful in many wireless sensor network applications. There is a well-known approach called FILA to process this kind of queries. Its basic idea is to install a filter at each sensor node to avoid unnecessary transmissions of sensor readings. FILA uses two algorithms to ensure the correctness and efficiency of the approach: a query reevaluation algorithm and a filter setting algorithm. In this paper, we propose improvements to each of these two algorithms. First, we propose a decentralized query reevaluation algorithm to reduce the communication cost of sending probe messages. Second, we propose a linear regression-based filter setting algorithm to improve the effectiveness of filters. Experimental results on real data traces show that our proposed improvements further enhance the performance of FILA in terms of network lifetime.

Rule-based Dynamic Business Process Modification and Adaptation

The advances in ubiquitous computing technologies have given rise to the u-Business, where we obtain business-related context information from ubiquitous devices to adapt to the changes of business environments in a timely manner. In these rapidly changing u-Business environments, we need a mechanism to modify business process schemas, which represent a group of business activities arranged in a meaningful way to give values to the u-Business. We also need a way to migrate running process instances to their new business process schemas. In this paper, we propose a rule-driven approach for dynamic business process schema modification and instance adaptation. The proposed approach uses rules to describe schema modification operations to be applied and rule execution conditions. Business environment changes are encoded in these rules, thus timely schema modification is possible. We also propose a system architecture for realizing this rule-driven approach. We have designed the system architecture by utilizing existing technologies so that the system can be easily implemented.

Analyzing the critical path for the well-formed workflow schema

Though the concept of critical path in the workflow is important because it can be utilzied in many issues in workflow systems, the critical path in the context of the workflow has not been much addressed in the past. In this paper we propose a method to systematically determine the critical path in a workflow schema built by the workflow control constructs described in our workflow model.