Jun-Ki Min

XPRESS: a queriable compression for XML data

Like HTML, many XML documents are resident on native file systems. Since XML data is irregular and verbose, the disk space and the network bandwidth are wasted. To overcome the verbosity problem, the research on compressors for XML data has been conducted. However, some XML compressors do not support querying compressed data, while other XML compressors which support querying compressed data blindly encode tags and data values using predefined encoding methods. Thus, the query performance on compressed XML data is degraded.In this paper, we propose XPRESS, an XML compressor which supports direct and efficient evaluations of queries on compressed XML data. XPRESS adopts a novel encoding method, called reverse arithmetic encoding, which is intended for encoding label paths of XML data, and applies diverse encoding methods depending on the types of data values. Experimental results with real life data sets show that XPRESS achieves significant improvements on query performance for compressed XML data and reasonable compression ratios. On the average, the query performance of XPRESS is 2.83 times better than that of an existing XML compressor and the compression ratio of XPRESS is 73%.

Parallel computation of skyline and reverse skyline queries using mapreduce

The skyline operator and its variants such as dynamic skyline and reverse skyline operators have attracted considerable attention recently due to their broad applications. However, computations of such operators are challenging today since there is an increasing trend of applications to deal with big data. For such data-intensive applications, the MapReduce framework has been widely used recently. In this paper, we propose efficient parallel algorithms for processing the skyline and its variants using MapReduce. We first build histograms to effectively prune out nonskyline (non-reverse skyline) points in advance. We next partition data based on the regions divided by the histograms and compute candidate (reverse) skyline points for each region independently using MapReduce. Finally, we check whether each candidate point is actually a (reverse) skyline point in every region independently. Our performance study confirms the effectiveness and scalability of the proposed algorithms.

Efficient extraction of schemas for XML documents

In this paper, we present a technique for efficient extraction of concise and accurate schemas for XML documents. By restricting the schema form and applying some heuristic rules, we achieve the efficiency and conciseness. The result of an experiment with real-life DTDs shows that our approach attains high accuracy and is 20 to 200 times faster than existing approaches.

Physical evaluation of popped cereal snacks with spent hen meat

Various blends of spent hen meat and grains (potato starch, corn starch, and rice flour) were popped using a popping machine. Lowest bulk density was observed in the snack with 1:2 ratio of meat and potato starch. Except for the popped snack with meat and rice flour, as the starch content increased, bulk density decreased gradually. Popped snacks with grains only were higher in L* value than those with meat and grains. The a* and b* values increased with increasing meat content. All popped snacks were significantly different (P<0.001) in bulk density, color, and breaking force. As the grain content of snacks increased, the size of the air cells also increased. Results of scanning electron microscopic and optical microscopic observations revealed the popping degree of snack with starch and spent hen meat was affected by the presence of meat. The optimum ratios of meat to grain for high expansion ratio were determined to be 1:2 and 1:3 of meat to corn starch and potato starch.

Effective ranking and search techniques for Web resources considering semantic relationships

On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.

EDGES: Efficient data gathering in sensor networks using temporal and spatial correlations

In this paper, we present an approximate data gathering technique, called EDGES, for sensor networks that utilizes temporal and spatial correlations. The goal of EDGES is to efficiently obtain the sensor reading within a certain error bound. To do this, EDGES utilizes the multiple model Kalman filter, which is for the non-linear data distribution, as an approximation approach. The use of the Kalman filter allows EDGES to predict the future value using a single previous sensor reading in contrast to the other statistical models such as the linear regression and multivariate Gaussian. In order to extend the lifetime of networks, EDGES utilizes the spatial correlation. In EDGES, we group spatially close sensors as a cluster. Since a cluster header in a network acts as a sensor and router, a cluster header wastes its energy severely to send its own reading and/or data coming from its children. Thus, we devise a redistribution method which distributes the energy consumption of a cluster header using the spatial correlation. In some previous works, the fixed routing topology is used or the roles of nodes are decided at the base station and this information propagates through the whole network. But, in EDGES, the change of a cluster is notified to a small portion of the network. Our experimental results over randomly generated sensor networks with synthetic and real data sets demonstrate the efficiency of EDGES.

An efficient XML encoding and labeling method for query processing and updating on dynamic XML data

In this paper, we propose an efficient encoding and labeling scheme for XML, called EXEL, which is a variant of the region labeling scheme using ordinal and insert-friendly bit strings. We devise a binary encoding method to generate the ordinal bit strings, and an algorithm to make a new bit string inserted between bit strings without any influences on the order of preexisting bit strings. These binary encoding method and bit string insertion algorithm are the bases of the efficient query processing and the complete avoidance of re-labeling for updates. We present query processing and update processing methods based on EXEL. In addition, the Stack-Tree-Desc algorithm is used for an efficient structural join, and the String B-tree indexing is utilized to improve the join performance. Finally, the experimental results show that EXEL enables complete avoidance of re-labeling for updates while providing fairly reasonable query processing performance.

Structural function inlining technique for structurally recursive XML queries

Structurally recursive XML queries are an important query class that follows the structure of XML data. At present, it is difficult for XQuery to type and optimize structurally recursive queries because of polymorphic recursive functions involved in the queries. In this paper, we propose a new technique called structural function inlining which inlines recursive functions used in a query by making good use of available type information. Based on the technique, we develop a new approach to typing and optimizing structurally recursive queries. The new approach yields a more precise result type for a query. Furthermore, it produces an optimal algebraic expression for the query with respect to the type information. When a structurally recursive query is applied to non-recursive XML data, our approach translates the query into a finitely nested iterations. We conducted several experiments with commonly used real-life and synthetic datasets. The experimental results show that the number of node lookups by our approach is on the average 3.7 times and up to 279.8 times smaller than that by the XQuery cores current approach in evaluating structurally recursive queries.

An intelligent query processing for distributed ontologies

In this paper, we propose an intelligent distributed query processing method considering the characteristics of a distributed ontology environment. We suggest more general models of the distributed ontology query and the semantic mapping among distributed ontologies compared with the previous works. Our approach rewrites a distributed ontology query into multiple distributed ontology queries using the semantic mapping, and we can obtain the integrated answer through the execution of these queries. Furthermore, we propose a distributed ontology query processing algorithm with several query optimization techniques: pruning rules to remove unnecessary queries, a cost model considering site load balancing and caching, and a heuristic strategy for scheduling plans to be executed at a local site. Finally, experimental results show that our optimization techniques are effective to reduce the response time.

An effective semantic search technique using ontology

In this paper, we present a semantic search technique considering the type of desired Web resources and the semantic relationships between the resources and the query keywords in the ontology. In order to effectively retrieve the most relevant top-k resources, we propose a novel ranking model. To do this, we devise a measure to determine the weight of the semantic relationship. In addition, we consider the number of meaningful semantic relationships between a resource and keywords, the coverage of keywords, and the distinguishability of keywords. Through experiments using real datasets, we observe that our ranking model provides more accurate semantic search results compared to existing ranking models.