• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.15 no.7
• /
• pp.1525-1530
• /
• 2011

Techniques for processing continuous queries are required to developing the various types of application services (monitoring services) in ubiquitous environment where the real-time data acquisition from a lot of sensors, analysis, and processing are required. In the previous works of the continuous queries, they have represented all of the continuous queries as the interval queries or region queries, and proposed some methods for processing theses queries. The types of continuous queries, however, are very various, and could be presented by combining the attribute conditions, spatial conditions, and temporal conditions. In this paper, I have classify the types of continuous queries, and have proposed the continuous query model which could be presented by combining those conditions. The contributions of this paper include that it proposes the query model representing the continuous queries and suggests future research directions.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.441-443
• /
• 2010

Techniques for processing continuous queries are required to developing the various types of application services in ubiquitous environment where the real-time data acquisition from a lot of sensors, analysis, and processing are required. In the previous works of the continuous queries, they have represented all of the continuous queries as the interval queries or region queries, and proposed some methods for processing theses queries. The types of continuous queries, however, are very various, and could be presented by combining the attribute conditions, spatial conditions, and temporal conditions. In this paper, I have classify the types of continuous queries, and have proposed the continuous query model which could be presented by combining those conditions. The contributions of this paper include that it proposes the query model representing the continuous queries and suggests future research directions.

• ETRI Journal
• /
• v.29 no.5
• /
• pp.691-693
• /
• 2007

Existing methods to process continuous range queries are not scalable. In particular, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We propose a novel query indexing method called the projected attribute bit (PAB)-based query index. We project a two-dimensional continuous range query on each axis to get two one-dimensional bit lists. Since the queries are transformed to bit lists and query evaluation is performed by bit operations, the storage cost of indexing and query evaluation time are reduced significantly. Through various experiments, we show that our method outperforms the containment-encoded squares-based indexing method, which is one of the most recently proposed methods.

• Journal of KIISE:Databases
• /
• v.33 no.3
• /
• pp.310-326
• /
• 2006

In this paper, we deal with a method of efficiently processing continuous queries in a data stream environment. We classify previous query processing methods into two dual categories - data-initiative and query-initiative - depending on whether query processing is initiated by selecting a data element or a query. This classification stems from the fact that data and queries have been treated asymmetrically. For processing continuous queries, only data-initiative methods have traditionally been employed, and thus, the performance gain that could be obtained by query-initiative methods has been overlooked. To solve this problem, we focus on an observation that data and queries can be treated symmetrically. In this paper, we propose the duality model of data and queries and, based on this model, present a new viewpoint of transforming the continuous query processing problem to a multi-dimensional spatial join problem. We also present a continuous query processing algorithm based on spatial join, named Spatial Join CQ. Spatial Join CQ processes continuous queries by finding the pairs of overlapping regions from a set of data elements and a set of queries defined as regions in the multi-dimensional space. The algorithm achieves the effects of both of the two dual methods by using the spatial join, which is a symmetric operation. Experimental results show that the proposed algorithm outperforms earlier methods by up to 36 times for simple selection continuous queries and by up to 7 times for sliding window join continuous queries.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.06c
• /
• pp.46-48
• /
• 2006

This paper addresses the problem of monitoring continuous k-nearest neighbor (k-NN) queries. Given a set of moving (or static) objects and a set of moving (or static) query points, monitoring continuous k-NN query retrieves and updates the closest k objects to a query point continually. In order to support location based services (LBSs) in highly dynamic environments, where objects and/or queries are frequently moving, monitoring continuous queries require real-time updated results when objects and/or queries change their locations. Thus, it is important to minimize time delay for maintaining up to date the results. In this paper, we present monitoring method to shorten time delay for updating continuous k-NN queries based on the notion of result region and the minimum bounding rectangle enclosing all objects in each cell, referred to as c-MBR, in the grid index structure. Simulations are conducted to show the efficiency of the proposed method.

• Journal of Computing Science and Engineering
• /
• v.5 no.4
• /
• pp.294-304
• /
• 2011

Enhancing continuous queries over data streams with temporal functions and predicates enriches the expressive power of those queries. While traditional continuous queries retrieve only the values of attributes, temporal continuous queries retrieve the valid time intervals of those values as well. Correctly evaluating such queries requires the coalescing of adjacent timestamps for value-equivalent tuples prior to evaluating temporal functions and predicates. For many stream applications, the available computing resources may be too limited to produce exact query results. These limitations are commonly addressed through load shedding and produce approximated query results. There have been many load shedding mechanisms proposed so far, but for temporal continuous queries, the presence of coalescing makes theses existing methods unsuitable. In this paper, we propose a new accuracy metric and load shedding algorithm that are suitable for temporal query processing when memory is insufficient. The accuracy metric uses a combination of the Jaccard coefficient to measure the accuracy of attribute values and $\mathcal{PQI}$ interval orders to measure the accuracy of the valid time intervals in the approximate query result. The algorithm employs a greedy strategy combining two objectives reflecting the two accuracy metrics (i.e., value and interval). In the performance study, the proposed greedy algorithm outperforms a conventional random load shedding algorithm by up to an order of magnitude in its achieved accuracy.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2010.05a
• /
• pp.923-926
• /
• 2010

A sensor network system processes user queries using the recent field data collected by each sensor node. To process user queries, the system propagates the queries to the specific sensor nodes which have the relevant data and aggregates the results of the queries. However, if continuous queries are processed by the existing scheme, the system has the problem where the queries are propagated repeatedly. In this paper, we propose the query processing scheme to process the continuous queries over the sensor streaming data. To do this, each sensor node builds its own query index on its node. And, we present the scheme to deal with the unexpected data rising on the sensor node.

• Journal of KIISE:Computing Practices and Letters
• /
• v.13 no.6
• /
• pp.397-401
• /
• 2007

Recent applications on continuous queries on moving objects are extended quickly to various parts. These applications need not only 2-dimensional space data but also high-dimensional space data. If we use previous index for overlapped continuous range queries on high-dimensional space data, as the number of continuous range queries on a large number of moving objects becomes larger, their performance degrades significantly. We focus on stationary queries, non-exponential increase of storage cost and efficient processing time for large data sets. In this paper, to solve these problems, we present a novel query indexing method, denoted as PAB(Projected Attribute Bit)-based query index. We transfer information of high-dimensional continuous range query on each axis into one-dimensional bit lists by projecting technique. Also proposed query index supports incremental update for efficient query processing. Through various experiments, we show that our method outperforms the CES(containment-encoded squares)-based indexing method which is one of the most recent research.

• 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.