Continuous Top-k Research Articles

Garden: a real-time processing framework for continuous top-k trajectory similarity search

Garden: a real-time processing framework for continuous top-k trajectory similarity search

Continuous top-k approximated join of streaming and evolving distributed data

Continuous top-k approximated join of streaming and evolving distributed data

EPF: A General Framework for Supporting Continuous Top-k Queries Over Streaming Data

Continuous top-k query over sliding window is a fundamental problem in the domain of streaming data management, which monitors the query window and retrieves k objects with the highest scores when the window slides. The key of supporting this query is maintaining a subset of objects in the window, and try to retrieve answers from them when the window slides. The state-of-the-art approach called SAP utilizes the partition technique to support top-k searches. Its key idea is using, as few as possible, high-quality candidates to support the query via finding a proper partition. However, it has to waste relatively high computation cost in evaluating whether the partition is proper and re-scanning the widow. In this paper, we propose an ELM-based framework named EPF, which improves SAP via learning the nature of streaming data. If we learn that the distribution of streaming data is predictable, we could construct a suitable prediction model for a more efficient partition of the window. Furthermore, we propose a novel algorithm to reduce the re-scanning cost. We conduct a thorough experimental study of this technique on real and synthetic datasets and show the significant performance improvement when applying the technique in existing algorithms.

Efficient continuous top-k geo-image search on road network

With the rapid development of mobile Internet and cloud computing technology, large-scale multimedia data, e.g., texts, images, audio and videos have been generated, collected, stored and shared. In this paper, we propose a novel query problem named continuous top-$k$ geo-image query on road network which aims to search out a set of geo-visual objects based on road network distance proximity and visual content similarity. Existing approaches for spatial textual query and geo-image query cannot address this problem effectively because they do not consider both of visual content similarity and road network distance proximity on road network. In order to address this challenge effectively and efficiently, firstly we propose the definition of geo-visual objects and continuous top-$k$ geo-visual objects query on road network, then develop a score function for search. To improve the query efficiency in a large-scale road network, we propose the search algorithm named geo-visual search on road network based on a novel hybrid indexing framework called VIG-Tree, which combines G-Tree and visual inverted index technique. In addition, an important notion named safe interval and results updating rule are proposed, and based on them we develop an efficient algorithm named moving monitor algorithm to solve continuous query. Experimental evaluation on real multimedia dataset and road network dataset illustrates that our solution outperforms state-of-the-art method.

Continuous Top-k Monitoring on Document Streams

Continuous Top-k Monitoring on Document Streams

SAP: Improving Continuous Top-K Queries Over Streaming Data

Continuous top- $k$ query over streaming data is a fundamental problem in database. In this paper, we focus on the sliding window scenario, where a continuous top- $k$ query returns the top- $k$ objects within each query window on the data stream. Existing algorithms support this type of queries via incrementally maintaining a subset of objects in the window and try to retrieve the answer from this subset as much as possible whenever the window slides. However, since all the existing algorithms are sensitive to query parameters and data distribution, they all suffer from expensive incremental maintenance cost. In this paper, we propose a self-adaptive partition framework to support continuous top- $k$ query. It partitions the window into sub-windows and only maintains a small number of candidates with highest scores in each sub-window. Based on this framework, we have developed several partition algorithms to cater for different object distributions and query parameters. To our best knowledge, it is the first algorithm that achieves logarithmic complexity w.r.t. $k$ for incrementally maintaining the candidate set even in the worst case scenarios.

Continuous Top-k Monitoring on Document Streams

The efficient processing of document streams plays an important role in many information filtering systems. Emerging applications, such as news update filtering and social network notifications, demand presenting end-users with the most relevant content to their preferences. In this work, user preferences are indicated by a set of keywords. A central server monitors the document stream and continuously reports to each user the top- $k$ documents that are most relevant to her keywords. Our objective is to support large numbers of users and high stream rates, while refreshing the top- $k$ results almost instantaneously. Our solution abandons the traditional frequency-ordered indexing approach. Instead, it follows an identifier-ordering paradigm that suits better the nature of the problem. When complemented with a novel, locally adaptive technique, our method offers (i) proven optimality w.r.t. the number of considered queries per stream event, and (ii) an order of magnitude shorter response time (i.e., time to refresh the query results) than the current state-of-the-art.

Approximate Continuous Top-k Query over Sliding Window

Continuous top-k query over sliding window is a fundamental problem in database, which retrieves k objects with the highest scores when the window slides. Existing studies mainly adopt exact algorithms to tackle this type of queries, whose key idea is to maintain a subset of objects in the window, and try to retrieve answers from it. However, all the existing algorithms are sensitive to query parameters and data distribution. In addition, they suffer from expensive overhead for incremental maintenance, and thus cannot satisfy real-time requirement. In this paper, we define a novel query named (e, δ)-approximate continuous top-k query, which returns approximate answers for top-k query. In order to efficiently support this query, we propose an efficient framework, named PABF (Probabilistic Approximate Based Framework), to support approximate top-k query over sliding window. We firstly maintain a self-adaptive pruning value, which could filter out newly arrived objects who have a probability less than 1 − δ of being a query result. For those objects that are not filtered, we combine them together, if the score difference among them is less than a threshold. To efficiently maintain these combined results, the framework PABF also proposes a multi-phase merging algorithm. Theoretical analysis indicates that even in the worst case, we require only logarithmic complexity for maintaining each candidate.

연속 Top-k 질의 모니터링을 위한 효율적인 알고리즘

연속 Top-k 질의 모니터링을 위한 효율적인 알고리즘

Threshold-Based Distributed Continuous Top-k Query Processing for Minimizing Communication Overhead

Threshold-Based Distributed Continuous Top-k Query Processing for Minimizing Communication Overhead

Sliding window top-k dominating query processing over distributed data streams

Preference query processing is important for a wide range of applications involving distributed databases, such as network monitoring, web-based systems, and market analysis. In such applications, data objects are generated frequently and massively, which presents an important and challenging problem of continuous query processing over distributed data stream environments. A top-k dominating query, which has been receiving much research attention recently, returns the k data objects that dominate the highest number of data objects in a given dataset, and due to its dominance-based ranking function, we can easily obtain superior data objects. An emerging requirement in distributed stream environments is an efficient technique for continuously monitoring top-k dominating data objects. Despite of this fact, no study has addressed this problem. In this paper, therefore, we address the problem of continuous top-k dominating query processing over distributed data stream environments. We present two algorithms that monitor the exact top-k dominating data and efficiently eliminate unqualified data objects for the result, which reduces both communication and computation costs. In addition to these algorithms, we present an approximate algorithm that further reduces both communication and computation costs. Extensive experiments on both synthetic and real data have demonstrated the efficiency and scalability of our algorithms.

Efficient continuous top-k spatial keyword queries on road networks

With the development of GPS-enabled mobile devices, more and more pieces of information on the web are geotagged. Spatial keyword queries, which consider both spatial locations and textual descriptions to find objects of interest, adapt well to this trend. Therefore, a considerable number of studies have focused on the interesting problem of efficiently processing spatial keyword queries. However, most of them assume Euclidean space or examine a single snapshot query only. This paper investigates a novel problem, namely, continuous top-k spatial keyword queries on road networks, for the first time. We propose two methods that can monitor such moving queries in an incremental manner and reduce repetitive traversing of network edges for better performance. Experimental evaluation using large real datasets demonstrates that the proposed methods both outperform baseline methods significantly. Discussion about the parameters affecting the efficiency of the two methods is also presented to reveal their relative advantages.

Continuous Top-k Contour Regions Querying in Sensor Networks

Wireless sensor networks (WSNs) are important parts of Internet of Things or Cyber-Physical Systems (CPS). WSNs can be seen as a new type of distributed database systems. The data query processing is very important for WSNs. In this paper, we proposed a Continuous Top-k Contour Regions Querying algorithm (CTCRQ) which can continuously obtain the top-k contour regions and does not lose the rate of precision (accuracy). We take full advantage of the kth value of top-k result in current round as the threshold to suppress the nodes whose readings do not belong to the top-k result in next round. Extensive experiments are conducted to evaluate the performance of the proposed CTCRQ approach by using a synthetic data set. The results provide a number of insightful observations and show that CTCRQ substantially outperforms Centralized algorithm, Centralized Optimized algorithm, and CCM algorithm in terms of data transmitted.

Evaluating continuous top-k queries over document streams

At the age of Web 2.0, Web content becomes live, and users would like to automatically receive content of interest. Popular RSS subscription approach cannot offer fine-grained filtering approach. In this paper, we propose a personalized subscription approach over the live Web content. The document is represented by pairs of terms and weights. Meanwhile, each user defines a top-k continuous query. Based on an aggregation function to measure the relevance between a document and a query, the user continuously receives the top-k most relevant documents inside a sliding window. The challenge of the above subscription approach is the high processing cost, especially when the number of queries is very large. Our basic idea is to share evaluation results among queries. Based on the defined covering relationship of queries, we identify the relations of aggregation scores of such queries and develop a graph indexing structure (GIS) to maintain the queries. Next, based on the GIS, we propose a document evaluation algorithm to share query results among queries. After that, we re-use evaluation history documents, and design a document indexing structure (DIS) to maintain the history documents. Finally, we adopt a cost model-based approach to unify the approaches of using GIS and DIS. The experimental results show that our solution outperforms the previous works using the classic inverted list structure.

Continuous Top-k Dominating Queries

Top-k dominating queries use an intuitive scoring function which ranks multidimensional points with respect to their dominance power, i.e., the number of points that a point dominates. The k points with the best (e.g., highest) scores are returned to the user. Both top-k and skyline queries have been studied in a streaming environment, where changes to the data set are very frequent. In such an environment, continuous query processing techniques are required toward efficient monitoring of query results, since periodic query re-execution is computationally intensive, and therefore, prohibitive. This work contains the first study of continuous top-k dominating queries over data streams. In comparison to continuous top-k and skyline queries, continuous top-k dominating queries pose additional challenges. Three exact algorithms (BFA, EVA, ADA) are studied, and among them ADA, which is enhanced with additional optimization techniques, shows the best overall performance. In some cases, we are willing to trade accuracy for speed. Toward this direction, two approximate algorithms are proposed (AHBA and AMSA). AHBA offers probabilistic guarantees regarding the accuracy of the result based on the Hoeffding bound, whereas AMSA performs a more aggressive computation resulting in more efficient processing. Evaluation results, based on real-life and synthetic data sets, show the efficiency and scalability of our techniques.

Top-k/w publish/subscribe: A publish/subscribe model for continuous top-k processing over data streams

Continuous processing of top-k queries over data streams is a promising technique for alleviating the information overload problem as it distinguishes relevant from irrelevant data stream objects with respect to a given scoring function over time. Thus it enables filtering of irrelevant data objects and delivery of top-k objects relevant to user interests in real-time. We propose a solution for distributed continuous top-k processing based on the publish/subscribe communication paradigm—top-k publish/subscribe over sliding windows (top-k/w publish/subscribe). It identifies k best-ranked objects with respect to a given scoring function over a sliding window of size w, and extends the publish/subscribe communication paradigm by continuous top-k processing algorithms coming from the field of data stream processing.In this paper, we introduce, analyze and evaluate the essential building blocks of distributed top-k/w publish/subscribe systems: first, we present a formal top-k/w publish/subscribe model and compare it to the prevailing Boolean publish/subscribe model. Next, we outline the top-k/w processing tasks performed by publish/subscribe nodes and investigate the properties of supported scoring functions. Furthermore, we explore potential routing strategies for distributed top-k/w publish/subscribe systems. Finally, we experimentally evaluate model properties and provide a comparative study investigating traffic requirements of potential routing strategies.

On Efficient Processing of Continuous Historical Top- $k$ Queries in Sensor Networks

The top-k query has long been an important topic in computer science. Efficient implementation of top-k queries is the key for information searching. In this paper, we develop the Efficient algorithm for the Continuous Historical Top-k (ECHT) extraction, which is a novel algorithm that can effectively process the continuous historical top-k query. A simple top-k extraction algorithm based on aggregation is used for user query processing, and two additional steps on the filter setting by which individual nodes do not have to report all their readings are proposed to further reduce communication cost. To the best of our knowledge, this is the first work for continuous historical top-k query processing in sensor networks, and our simulation results show that our schemes can reduce the total communication cost by up to two orders of magnitude, as compared with the centralized scheme or a straightforward extension from the previous top-k algorithm on a continuous monitoring query.