• The KIPS Transactions:PartD
• /
• v.14D no.3 s.113
• /
• pp.273-284
• /
• 2007

RFID middleware systems collect and filter the RFID streaming data gathered continuously by numerous readers in order to process requests from applications. These requests are called continuous queries because they are kept on executing during certain periods. To enhance the performance of the middleware, it is required to build an index to process the continuous queries efficiently. Several approaches of building an index on not data records but queries, called Query Index, are proposed and widely used for evaluating continuous queries over streaming data. The EPCglobal proposed an Event Cycle Specification (ECSpec) model, which is a standard query interface for RFID applications. Continuous queries based on ECSpec consist of a large number of segments for representing the query conditions. The problem with using any of the existing query indexes on these continuous queries is that it takes a long time to build the index because it is necessary to insert a large number of segments into the index. To solve this problem, we propose an Aggregate Transformation that converts a group of segments into a compressed data which is representative of the segments. We compare the performance of a transformed index with the existing query indexes.

• The KIPS Transactions:PartD
• /
• v.11D no.7 s.96
• /
• pp.1375-1384
• /
• 2004

Streaming data processing is an area of interest with much research under way. There has been increasing attention on the demands for efficient processing of streaming data produced in the application areas such as monitoring and sensor network. We have developed a continuous query processing system for streaming data and evaluated its performance in this paper. XML, the standard for data exchange on the web, is used as the model for the streaming data and the XQuery appended with a time interval is adopted as the query language for expressing con-tinuous queries. In the proposed system, the result is produced through background processing and materialized for reute in subsequent query processing. Through a detailed set of performance experiments, we shoed the effectiveness of the proposed system.

• Proceedings of the IEEK Conference
• /
• 2007.07a
• /
• pp.133-134
• /
• 2007

The effective data processing mechanism in the sensor network means data stream model and real-time query processing model for real-time processing of stream data. This mechanism can improve satisfaction of users and reduce delay rate of data processing. In this paper, we explain the problem which is occurred when users need to search certain information among information of stream data and describe reduction model of delay rate according to data transmission.

• Journal of KIISE:Databases
• /
• v.33 no.6
• /
• pp.563-577
• /
• 2006

A stream processor uses resource sharing method for efficient of limited resource in multiple continuous queries. The previous methods process aggregate queries to consist the level structure. So insert operation needs to reconstruct cost of the level structure. Also a search operation needs to search cost of aggregation information in each size of sliding windows. Therefore this paper uses linear structure for optimization of sliding window aggregations. The method comprises of making decision, generation and deletion of panes in sequence. The decision phase determines optimum pane size for holding accurate aggregate information. The generation phase stores aggregate information of data per pane from stream buffer. At the deletion phase, panes are deleted that are no longer used. The proposed method uses resources less than the method where level structures were used as data structures as it uses linear data format. The input cost of aggregate information is saved by calculating only pane size of data though numerous stream data is arrived, and the search cost of aggregate information is also saved by linear searching though those sliding window size is different each other. In experiment, the proposed method has low usage of memory and the speed of query processing is increased.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.4
• /
• pp.47-58
• /
• 2008

With the recent development of the ubiquitous computing technology, there are increasing interest and research in technologies such as sensors and RFID related to information recognition and location positioning in various ubiquitous fields. Especially, a standard specification was required for compatibility and interoperability in various RTLS(Real-Time Locating Systems) according to the development of RTLS to provide location and status information of moving objects using the RFID Tag. For these reasons, the ISO/IEC published the RTLS standard specification for compatibility and interoperability in RTLS. Therefore, in this paper, we designed and im plemented RTLS based on the spatial DSMS(Data Stream Management Stream) for efficiently managing and searching the incoming data stream of moving objects. The spatial DSMS is an extended system of STREAM(STanford stREam datA Manager) developed by Standford University to make various spatial operations possible. RTLS based on the spatial DSMS uses the SOAP(Simple Object Access Protocol) message between client and server for interoperability and translates client's SOAP message into CQL(Continuous Query Language) of the spatial DSMS. Finally, we proved the efficiency of RTLS based on the spatial DSMS by applying it for the staff location management service.

• Journal of Information Technology Services
• /
• v.16 no.1
• /
• pp.73-82
• /
• 2017

Recently in-memory data stream processing has been actively applied to various subjects such as query processing, OLAP, data mining, i.e., frequent item sets, association rules, clustering. However, finding regular periodic patterns of events in an infinite data stream gets less attention. Most researches about finding periods use autocorrelation functions to find certain changes in periodic patterns, not period itself. And they usually find periodic patterns in time-series databases, not in data streams. Literally a period means the length or era of time that some phenomenon recur in a certain time interval. However in real applications a data set indeed evolves with tiny differences as time elapses. This kind of a period is called as a pseudo-period. This paper proposes a new scheme called FPMH (Finding Periods using Multiple Hash functions) algorithm to find such a set of pseudo-periods over a data stream based on multiple hash functions. According to the type of pseudo period, this paper categorizes FPMH into three, FPMH-E, FPMH-PC, FPMH-PP. To maximize the performance of the algorithm in the data stream environment and to keep most recent periodic patterns in memory, we applied decay mechanism to FPMH algorithms. FPMH algorithm minimizes the usage of memory as well as processing time with acceptable accuracy.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.46 no.4
• /
• pp.1-12
• /
• 2009

An RFID application needs to collect product's information scattered over the RFID network efficiently according to the globalization of RFID applied enterprises. To be informed of the stock status of products promptly in the supply chain network, especially, it is necessary to support queries that retrieve statistical information of tagged products. Since existing RFID network does not provide these kinds of queries, however, an application should request a query to several RFID middleware systems and analyze collected data directly. This process makes an application do a heavy computation for retrieving statistical information. To solve this problem, we define a new Distributed Continuous Query that finds information of tagged products from the global RFID network and provides statistical information to RFID applications. We also propose a Distributed Continuous Query System to process the distributed continuous query efficiently. To find out the movement of products via multiple RFID systems in real time, our proposed system uses Pedigree which represents trade information of items. Our system can also reduce the cost of query processing for removing duplicated data from multiple middleware systems by using Pedigree.

• The KIPS Transactions:PartD
• /
• v.13D no.3 s.106
• /
• pp.317-324
• /
• 2006

In this paper, we propose a new indexing technique to solve various queries which have a strong good point not only database indexing schema take advantage of converting from semi-structured data to structured data but also performance is more faster than before. We represent structure information of XML document between nodes of tree that additional numbering information which can be bit-stream without modified structure of XML tree. And, We add in indexing schema searching incidental structure information in the process. In Querying schema, we recover ancestor nodes through give information of node using indexing schema in complete path query expression as well as relative path query expression. Therefore, it takes advantage of making derivative query expression with given query. In this process, we recognize that indexing and querying schema can get searched result set faster and more accurate. Because response time is become shorter by bit operating, when query occur and it just needs information of record set earch node in database.

• The Journal of Society for e-Business Studies
• /
• v.18 no.3
• /
• pp.159-175
• /
• 2013

Recently, as real-time availability of e-commerce data becomes possible, the requirement of real-time analysis of e-commerce increases significantly. In the real-time analysis of e-commerce data, it is very important to efficiently process continuous join queries between an e-commerce data stream and disk-based large relations. In this paper, we propose an efficient method for processing a continuous join query between an e-commerce data stream and multiple disk-based relations. The proposed method improves the service rate significantly, while reducing the amount of required memory substantially. Through analysis and various experiments, we show the efficiency of the proposed method compared with the previous one in terms of service rate and memory usage.

• Journal of Korea Spatial Information System Society
• /
• v.10 no.4
• /
• pp.97-102
• /
• 2008

A wireless sensor network consists of many sensors that collect and transmit physical or environmental conditions at different locations to a server continuously. Many researches mainly focus on processing continuous queries on real-time data stream. However, they do not concern the problem of storing the historical data, which is mandatory to the historical queries. In this paper, we propose two time-based storage methods to store the sensor data stream and reduce the managed tuples without any loss of information, which lead to the improvement of the accuracy of query results.