Chang-Sup Park

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.

Finding an efficient rewriting of OLAP queries using materialized views 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 to rewrite a given OLAP query using various kinds of materialized views which already exist in data warehouses. We first define the normal forms of OLAP queries and materialized views based on the selection and aggregation granularities, which are derived from the lattice of dimension hierarchies. Conditions for usability of materialized views 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 can effectively utilize materialized views having different selection granularities, selection regions, and aggregation granularities together. We also propose an algorithm to find a set of materialized views that results in a rewritten query which can be executed efficiently. We show the effectiveness and performance of the algorithm experimentally.

Efficient processing of keyword queries over graph databases for finding effective answers

Abstract In this paper, we study on effective and efficient processing of keyword-based queries over graph databases. To produce more relevant answers to a query than the previous approaches, we suggest a new answer tree structure which has no constraint on the number of keyword nodes chosen for each keyword in the query. For efficient search of answer trees on the large graph databases, we design an inverted list index to pre-compute and store connectivity and relevance information of nodes to keyword terms in the graph. We propose a query processing algorithm which aggregates from the pre-constructed inverted lists the best keyword nodes and root nodes to find top- k answer trees most relevant to the given query. We also enhance the method by extending the structure of the inverted list and adopting a relevance lookup table, which enables more accurate estimation of the relevance scores of candidate root nodes and efficient search of top- k answer trees. Performance evaluation by experiments with real graph datasets shows that the proposed method can find more effective top- k answers than the previous approaches and provides acceptable and scalable execution performance for various types of keyword queries on large graph databases.

Attribute summarization: a technique for wireless XML streaming

Recently, wireless mobile computing has been realized in the industry, where mobile clients communicate by using their handheld devices. Meanwhile, data broadcasting is an effective way for data dissemination due to its beneficial characteristics such as bandwidth efficiency, energy-efficiency, and scalability. In this paper, we propose an XML stream optimization method for time critical data, which is highly dependant to the time. To end this, we utilize structural index to integrate the elements of same path into one node. Furthermore, we propose an attribute summarization strategy to minimize the size of the XML steam by classifying attribute names and values. Experimental results show that our method outperforms the previous wireless XML broadcast methods in terms of energy and latency-efficiency.

Usability-based caching of query results in OLAP systems

In this paper we propose a new cache management scheme for online analytical processing (OLAP) systems based on the usability of query results in rewriting and processing other queries. For effective admission and replacement of OLAP query results, we consider the benefit of query results not only for recently issued queries but for the expected future queries of a current query. We exploit semantic relationships between successive queries in an OLAP session, which are derived from the interactive and navigational nature of OLAP query workloads, in order to classify and predict subsequent future queries. We present a method for estimating the usability of query results for the representative future queries using a probability model for them. Experimental evaluation shows that our caching scheme using the past and future usability of query results can reduce the cost of processing OLAP query workloads effectively only with a small cache size and outperforms the previous caching strategies for OLAP systems.

An efficient void resolution method for geographic routing in wireless sensor networks

Geographic routing is an attractive choice for routing data in wireless sensor networks because of lightweight and scalable characteristics. Most geographic routing approaches combine a greedy forwarding scheme and a void resolution method to detour a void area that has no active sensor. The previous solutions, including the well-known GPSR protocol, commonly use the right-hand rule for void resolution. However, the detour path produced by the right-hand rule is not energy-efficient in many cases. In this paper, we propose a new void resolution method, called void resolution-forwarding, which can overcome voids in the sensor network energy-efficiently. It exploits the quadrant-level right-hand rule to select the next hop for the current node during circumventing a void area. We show by experiments that the proposed method is efficient and scalable with respect to the various voids and network density and that it outperforms the GPSR protocol significantly.

An Effective Keyword Search Method for Graph-Structured Data Using Extended Answer Structure

This paper proposes an effective approach to ranked keyword search over graph-structured data which is getting much attraction in various applications. To provide more effective search results than the previous approaches, we suggest an extended answer structure which has no constraint on the number of keyword nodes and is based on a new relevance measure. For efficient keyword search, we also use an inverted list index which pre-computes connectivity and relevance information on the nodes in the graph. We present a query processing algorithm based on the pre-constructed inverted lists, which aggregates entries relevant to each node and finds top-k answer trees relevant to the given query. We also enhance the basic search method by storing additional information on the relevance of the related entries in the lists, in order to estimate the relevance score of each node more closely and to find top-k answers more efficiently. We show by experiments that the proposed keyword search method can provide effective top-k search results over large amount of graph-structured data with good execution performance.