• KIISE Transactions on Computing Practices
• /
• v.21 no.1
• /
• pp.76-81
• /
• 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
• /
• v.36 no.3
• /
• pp.189-197
• /
• 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
• /
• v.36 no.4
• /
• pp.306-313
• /
• 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.

• Journal of KIISE:Databases
• /
• v.31 no.2
• /
• pp.132-139
• /
• 2004

A range $Top-\kappa$ query returns top k records in order of a measure attribute within a specified region on multi-dimensional data, and it is a powerful tool for analysis in spatial databases and data warehouse environments. In this paper, we propose an algorithm for answering the query via selective traverse of a Hierarchical Max R-Tree(HMR-tree). It is possible to execute the query by accessing only a small part of the leaf nodes in the query region, and the query performance is nearly constant regardless of the size of the query region. The algorithm manages the priority queue efficiently to reduce cost of handling the queue and the proposed HMR-tree can guarantee the same fan-out as the original R-tree.

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

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

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

• The Journal of the Korea Contents Association
• /
• v.17 no.11
• /
• pp.209-220
• /
• 2017

As a semantic data is widely used in various applications such as Knowledge Bases and Semantic Web, needs for effective search over a large amount of RDF data have been increasing. Previous keyword search methods based on distinct root semantics only retrieve a set of answer trees having different root nodes. Thus, they often find answer trees with similar meanings or low query relevance together while those with the same root node cannot be retrieved together even if they have different meanings and high query relevance. We propose a new method to find diverse and relevant answers to the query by permitting duplication of root nodes among them. We present an efficient query processing algorithm using path indexes to find top-k answers given a maximum amount of root duplication a set of answer trees can have. We show by experiments using a real dataset that the proposed approach can produce effective answer trees which are less redundant in their content nodes and more relevant to the query than the previous method.

• ETRI Journal
• /
• v.16 no.4
• /
• pp.27-47
• /
• 1995

This paper improves top-down execution models of logic programs based on a two-phase abstract interpretation which consists of a bottom-up analysis followed by a top-down one. The two-phase analysis provides an approximation of all (possibly non-ground) success patterns of clauses relevant to a query. It is specialized by considering Sato and Tamaki’s depth k abstraction as abstract function. By the ability of the analysis to approximate possibly non-ground success patterns of clauses relevant to a query, it can be statically determined whether some subgoals will fail during execution and some succeeding subgoals do not participate in success patterns of program clauses relevant to a given query. These properties are utilized to improve execution models. This approach can be easily applied to any top-down (parallel) execution models. As instances, it is shown to be applicable to linear execution model and AND/OR Process Model.

• Journal of KIISE
• /
• v.43 no.5
• /
• pp.590-595
• /
• 2016

In this study, we propose an efficient method for monitoring continuous top-k queries. In contrast to the conventional top-k queries, the presented top-k query considers both spatial and non-spatial attributes. We proposed a novel main-memory based grid access method, called Bit-Vector Grid Index (BVGI). The proposed method quickly identifies whether the moving objects are included in some of the grid cell by encoding a non-spatial attribute value of the moving object to bit-vector. Experimental simulations demonstrate that the proposed method is several times faster than the previous method and uses considerably less memory.