• KIISE Transactions on Computing Practices
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• v.21 no.1
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• pp.76-81
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• 2015

Research on top-k query processing algorithms for analyzing big data have been spotlighted recently. However, because existing top-k query processing algorithms do not provide an efficient index structure, they incur high query processing costs and cannot support various types of queries. To solve these problems, we propose a top-k query processing algorithm using a view selection method based on a grid index. The proposed algorithm reduces the query processing time by retrieving the minimum number of grid cells for the query range, by using a grid index-based view selection method. Finally, we show from our performance analysis that the proposed scheme outperforms an existing scheme, in terms of both query processing time and query result accuracy.

• Journal of KIISE:Databases
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• v.36 no.3
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• pp.189-197
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• 2009

Top-k queries are important to many wireless sensor applications. Conventional Top-k query processing algorithms install a filter at each sensor node and suppress unnecessary sensor updates. However, they have some drawbacks that the sensor nodes consume energy extremely to probe sensor reading or update filters. Especially, it becomes worse, when the variation ratio of top-k result is higher. In this paper, we propose a novel Top-k query processing algorithm for energy-efficiency. First, each sensor determines its priority as the order of data gathering. Next, sensor nodes that have higher priority transmit their sensor readings to the base station until gathering k sensor readings. In order to show the superiority of our query processing algorithm, we simulate the performance with the existing query processing algorithms. As a result, our experimental results show that the network lifetime of our method is prolonged largely over the existing method.

• Journal of KIISE:Databases
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• v.36 no.4
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• pp.306-313
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• 2009

Top-k queries are issued to find out the highest (or lowest) readings in many sensor applications. Many top-k query processing algorithms are proposed to reduce energy consumption; FILA installs a filter at each sensor node and suppress unnecessary sensor updates; PRIM allots priorities to sensor nodes and collects the minimal number of sensor reading according to the priorities. However, if many sensor reading converge into the same range of sensor values, it leads to a problem that many false positives are occurred. In this paper, we propose a cluster-based approach to reduce them effectively. Our proposed algorithm operates in two phases: top-k query processing in the cluster level and top-k query processing in the tree level. False positives are effectively filtered out in each level. Performance evaluations show that our proposed algorithm reduces about 70% false positives and achieves about 105% better performance than the existing top-k algorithms in terms of the network lifetime.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.5153-5170
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• 2016

The benefit of the scalability and flexibility inherent in cloud computing motivates clients to upload data and computation to public cloud servers. Because data is placed on public clouds, which are very likely to reside outside of the trusted domain of clients, this strategy introduces concerns regarding the security of sensitive client data. Thus, to provide sufficient security for the data stored in the cloud, it is essential to encrypt sensitive data before the data are uploaded onto cloud servers. Although data encryption is considered the most effective solution for protecting sensitive data from unauthorized users, it imposes a significant amount of overhead during the query processing phase, due to the limitations of directly executing operations against encrypted data. Recently, substantial research work that addresses the execution of SQL queries against encrypted data has been conducted. However, there has been little research on top-k join query processing over encrypted data within the cloud computing environments. In this paper, we develop an efficient algorithm that processes a top-k join query against encrypted cloud data. The proposed top-k join processing algorithm is, at an early phase, able to prune unpromising data sets which are guaranteed not to produce top-k highest scores. The experiment results show that the proposed approach provides significant performance gains over the naive solution.

• Journal of Korea Multimedia Society
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• v.13 no.9
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• pp.1299-1313
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• 2010

Top-k queries return k objects that users most want in the database. The Partitioned-Layer Index (simply, the PL -index) is a representative method for processing the top-k queries efficiently. The PL-index partitions the database into a number of smaller databases, and then, for each partitioned database, constructs a list of sublayers over the partitioned database. Here, the $i^{th}$ sublayer in the partitioned database has the objects that can be the top-i object in the partitioned one. To retrieve top k results, the PL-index merges the sublayer lists depending on the user's query. The PL-index has the advantage of reading a very small number of objects from the database when processing the queries. However, since many random accesses occur in merging the sublayer lists, query performance of the PL-index is not good in environments like disk-based databases. In this paper, we propose the Abstracted Partitioned-Layer Index (simply, the APL-index) that significantly improves the query performance of the PL-index in disk-based environments by reducing the number of random accesses. First, by abstracting each sublayer of the PL -index into a virtual (point) object, we transform the lists of sublayers into those of virtual objects (ie., the APL-index). Then, we virtually process the given query by using the APL-index and, accordingly, predict sublayers that are to be read when actually processing the query. Next, we read the sublayers predicted from each sublayer list at a time. Accordingly, we reduce the number of random accesses that occur in the PL-index. Experimental results using synthetic and real data sets show that our APL-index proposed can significantly reduce the number of random accesses occurring in the PL-index.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.9
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• pp.347-356
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• 2015

Due to the rapid development of IoT(Internet of Things) technology, traditional taxis are connected through dispatchers and location systems. Typically, modern taxis have embedded with GPS(Global Positioning System), which aims for obtaining the route information. By analyzing the frequency of taxi trip events, we can find the frequent route for a given query time. However, a scalability problem would occur when we convert the raw location data of taxi trip events into the analyzed frequency information due to the volume of location data. For this problem, we propose a NoSQL based top-K query system for taxi trip events. First, we analyze raw taxi trip events and extract frequencies of all routes. Then, we store the frequency information into hash-based index structure of MongoDB which is a document-oriented NoSQL database. Efficient top-K query processing for frequent route is done with the top of the MongoDB. We validate the efficiency of our algorithms by using real taxi trip events of New York City.

• Proceedings of the Korea Information Processing Society Conference
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• 2005.05a
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• pp.51-54
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• 2005

This paper proposes an index structure which is used to process X-Path on S-XML data. There are many previous index structures based on tree structure for X-Path processing. Because of general tree index's top-down query fashion, the unnecessary node traversal makes heavy access and decreases the query processing performance. And both of the two query types for X-Path called single-path query and branching query need to be supported in proposed index structure. This method uses a combination of path summary and the node indexing. First, it manages hashing on hierarchy elements which are presented in tag in S-XML. Second, array blocks named path summary array is created in each node of hashing to store the path information. The X-Path processing finds the tag element using hashing and checks array blocks in each node to determine the path of query's result. Based on this structure, it supports both single-path query and branching path query and improves the X-Path processing performance.

• KIPS Transactions on Software and Data Engineering
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• v.4 no.12
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• pp.543-548
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• 2015

Recently top-k query processing has been extremely important along with the explosion of data produced by a variety of applications. Top-k queries return the best k results ordered by a user-provided monotone scoring function. As cloud computing service has been getting more popular than ever, a hot attention has been paid to cloud-based data outsourcing in which clients' data are stored and managed by the cloud. The cloud-based data outsourcing, though, exposes a critical secuity concern of sensitive data, resulting in the misuse of unauthorized users. Hence it is essential to encrypt sensitive data before outsourcing the data to the cloud. However, there has been little attention to efficient top-k processing on the encrypted cloud data. In this paper we propose a novel top-k processing algorithm that can efficiently process a large amount of encrypted data in the cloud. The main idea of the algorithm is to prune unpromising intermediate results at the early phase without decrypting the encrypted data by leveraging an order-preserving encrypted technique. Experiment results show that the proposed top-k processing algorithm significantly reduces the overhead of client systems from 10X to 10000X.

• The Journal of the Korea Contents Association
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• v.20 no.8
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• pp.42-53
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• 2020

With the development of social network services, graph structures have been utilized to represent relationships among objects in various applications. Recently, a demand of subgraph matching in real-time graph streams has been increased. Therefore, an efficient approximate Top-k subgraph matching scheme for low latency in real-time graph streams is required. In this paper, we propose an approximate Top-k subgraph matching scheme considering data reuse in graph stream environments. The proposed scheme utilizes the distributed stream processing platform, called Storm to handle a large amount of stream data. We also utilize an existing data reuse scheme to decrease stream processing costs. We propose a distance based summary indexing technique to generate Top-k subgraph matching results. The proposed summary indexing technique costs very low since it only stores distances among vertices that are selected in advance. Finally, we provide k subgraph matching results to users by performing an approximate Top-k matching on the summary indexing. In order to show the superiority of the proposed scheme, we conduct various performance evaluations in diverse real world datasets.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.1073-1074
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• 2012

최근 인터넷의 발달과 사용량의 증가로 데이터의 양이 급증함에 따라 대용량 데이터를 효율적으로 검색하는 top k 질의 처리가 중요시 되고 있다. Layer 기반 방법은 가장 잘 알려진 top k 질의처리 방법이며, 객체의 모든 속성의 값들을 이용하여 객체들을 layer들의 리스트로 구성하는 방법이다. 본 논문에서는 그 중에서 convex hull을 사용하여 layer list를 생성하는 기존 연구를 조사하고 문제점을 파악한다.