• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.11-13
• /
• 2007

There has been much interest in stream query processing. Various index techniques and advanced join techniques have been proposed to efficiently process data stream queries. Previous proposals support rapid and advanced response to the data stream queries. However, the amount of data stream is increasing and the data stream query processing needs more speedup than before. In this paper, we proposed novel query processing techniques for large number of incoming documents stream. We proposed Dissection Technique for efficient query processing in the data stream environment. We focused on the dissection technique in join query processing. Our technique shows efficient operation performance comparing with the other proposal in the data stream. Proposed technique is applied to the sensor network system and XML database.

• Journal of Digital Contents Society
• /
• v.8 no.4
• /
• pp.541-546
• /
• 2007

Traditional query processing method is not efficient for continuous queries with large continuous stream data. This paper proposes a data-driven query processing method for stream data. The structure of query plan and query execution method are presented. With the proposed method, multiple query processing and sharing among queries can be achieved. Also query execution time can be reduced by storing partial results of query execution. This paper showed an example of query processing with XML data and XQuery query.

• Journal of KIISE:Databases
• /
• v.33 no.3
• /
• pp.327-341
• /
• 2006

As we are getting to deal with more applications that generate streaming data such as sensor network, monitoring, and SDI (selective dissemination of information), active research is being conducted to support efficient processing of queries over streaming data. The applications on the Web environment like SDI, among others, require query processing over streaming XML data, and its investigation is very important because XML has been established as the standard for data exchange on the Web. One of the major problems with the previous systems that support query processing over streaming XML data is that they cannot deal adaptively with dynamically changing stream because they rely on static query plans. On the other hand, the stream query processing systems based on relational data model have achieved adaptiveness in query processing due to query operator routing. In this paper, we propose a system of adaptive query processing over streaming XML data in which the model of adaptive query processing over streaming relational data is applied. We compare our system with YFiiter, one of the representative systems that provide XML stream query processing capability, to show efficiency of our system.

• International journal of advanced smart convergence
• /
• v.10 no.2
• /
• pp.64-72
• /
• 2021

XML message brokers have a lot of importance because XML has become a practical standard for data exchange in many applications. Message brokers covered in this document store many users. This paper is a study of the processing of twig pattern queries in XML documents using branching node streams in XML message broker structures. This work is about query processing in XML documents, especially for query processing with XML twig patterns in the XML message broker structure and proposed a method to reduce query processing time when parsing documents with XML twig patterns by processing information. In this paper, the twig pattern query processing method of documents using the branching node stream removes the twigging value of the branch node that does not include the labeling value of the branch node stream when it receives a twig query from the client. In this paper, the leaf node discovery time can be reduced by reducing the navigation time of nodes in XML documents that are matched to leaf nodes in twig queries for client twig queries. Overall, the overall processing time to respond to queries is reduced, allowing for rapid question-answer processing.

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.2
• /
• pp.69-77
• /
• 2009

Stream service environment demands real-time query processing for voluminous data which are ceaselessly delivered from tremendous sources. Typical R-tree based query processing technologies cannot efficiently handle such situations, which require repetitive and inefficient exploration from the tree root on every data event. However, many stream data including sensor readings show high locality, which we exploit to reduce the search space of queries to explore. In this paper, we propose a query processing scheme exploiting the locality of stream data. From the simulation, we conclude that the proposed scheme performs much better than the traditional ones in terms of scalability and exploration efficiency.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.3-5
• /
• 2007

Many continuous queries are important to be process efficiently in a data stream environment. It is applied a query index technique that takes linear performance irrespective of the number and width of intervals for processing many continuous queries. Previous researches are not able to support the dynamic insertion and deletion to arrange intervals for constructing an index previously. It shows that the insertion and search performance is slowed by the number and width of interval inserted. Many intervals have to be inserted and searched linearly in a data stream environment. Therefore, we propose Hashed Multiple Lists in order to process continuous queries linearly. Proposed technique shows fast linear search performance. It can be utilized the systems applying a sensor network, and preprocessing technique of spatiotemporal data mining.

• ETRI Journal
• /
• v.31 no.4
• /
• pp.463-465
• /
• 2009

In this letter, we present an event stream processing system that can evaluate a pattern query for a data sequence with predicates. We propose a pattern query language and develop a pattern query processing system. In our system, we propose novel techniques for run-time aggregation and negation processing and apply our system to stream data generated from vehicles to monitor unusual driving patterns.

• The KIPS Transactions:PartD
• /
• v.15D no.3
• /
• pp.297-304
• /
• 2008

Due to the proliferation of the Internet and intranet, new application domains such as stream data processing have emerged. Stream data is real-timely and continuously generated. In stream data environments, a lot of queries are registered, and then, the arrived data item is evaluated by registered queries. Thus, to accelerate the query performance, diverse continuous query index schemes have been proposed for stream data processing systems. In this paper, we focus on the query index technique for stream data. In general, a stream query contains the range condition. Thus, by using range conditions, the queries can be indexed. In this paper, we propose an efficient query index scheme, called QUISIS, using a modified Interval Skip Lists to accelerate search time. QUISIS utilizes a locality where a value which will arrive in near future is similar to the current value. Through the experimental study, we show the efficiency of our proposed method.

• Journal of the Korea Society of Computer and Information
• /
• v.13 no.1
• /
• pp.65-73
• /
• 2008

The telematics data management deals with queries on stream data coming from moving cars. So the stream DBMS should process the large amount of data stream in real-time. In this article, previous research projects are analyzed in the aspects of query processing. And a hybrid model is introduced where query preprocessor is used to process all types of queries in one singe system. Decreasing cost and rapidly increasing Performance of devices may guarantee the utmost parallelism of the hybrid system. As a result, various types of stream DBMS queries could be processed in a uniform and efficient way in a single system.

• The KIPS Transactions:PartD
• /
• v.16D no.1
• /
• pp.27-42
• /
• 2009

In realizing ubiquitous computing, techniques of efficiently using the limited resource at client such as mobile devices are required. With a mobile device with limited amount of memory, the techniques of XML stream query processing should be employed to process queries over a large volume of XML data. Recently, several techniques were proposed which fragment XML documents into XML fragments and stream them for query processing at client. During query processing, there could be great difference in resource usage (query processing time and memory usage) depending on how the source XML documents are fragmented. As such, an efficient fragmentation technique is needed. In this paper, we propose an XML fragmentation technique whereby resource efficiency in query processing at client could be enhanced. For this, we first present a cost model of query processing over XML fragment stream. Then, we propose an algorithm for resource-efficient XML fragmentation. Through implementation and experiments, we showed that our fragmentation technique outperformed previous techniques both in processing time and memory usage. The contribution of this paper is to have made the techniques of query processing over XML fragment stream more feasible for practical use.