Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 4,033 papers for “continuous queries”

Continuous Queries

Martin Hirzel · Encyclopedia of Big Data Technologies · 2019

Cited by 1

CONTINUOUS QUERIES

Mohamed A. Sharaf, Mohamed A. Sharaf, Mohamed A. Sharaf · 2007

Cited by 0

Continuous Queries

Shashi Shekhar, Hui Xiong, Xun Zhou · Encyclopedia of GIS · 2017

Cited by 0

Trading Continuous Queries

Jin Cheng, Ningning Ding, John C. S. Lui et al. · IEEE Transactions on Mobile Computing · 2025

In the bigdata era, data trading significantly enhances data-driven decision-making by facilitating data sharing. Streaming data from sources such as mobile devices and social media platforms creates new opportunities and challenges for data trading. Traditional data trading methods, designed for one time queries over static data base snapshots, neglect the growing need for trading continuous queries over streaming data. If applied directly to continuous queries, existing methods often result in repeated and imprecise charges that reduce the seller's profit, as they do not consider computation sharing during continuous query execution. To address these challenges, we propose CQ Trade, the first mechanism for continuous query based data trading, which incorporates computation sharing in query execution and integrates seamlessly with existing trading mechanisms. Our contributions are threefold: (1) we provide a theoretical analysis of prevalent computation-sharing techniques, including costmodeling and closed-form computation-sharing strategy derivation; (2) we formulate a general optimization problem to maximize the seller's profit, adaptable to various computation-sharing techniques; (3) we identify that our op timization problem merges vector bin packing and multidimensional knapsack challenges, and we tackle this complexity with a tailored branch-and-price algorithm that decomposes the problem in to a master problem and multiple sub-problems, achieving a globally optimal solution. Evaluation shows CQ Trade improve strading success rate by 12.8% and increases seller profit by 28.7% compared to traditional methods.

Cited by 0

Optimizing multiple continuous queries

Jaime Carbonell, Chun Jin · 2006

Emerging data stream processing applications present new challenges that are not addressed by traditional DBMS technologies. To provide practical solutions for matching highly dynamic data streams with multiple long-lived and dynamically-updated continuous queries, a stream processing system should support incremental evaluation over new data, query optimization for continuous queries including computation sharing among multiple queries. This thesis addresses these problems, presents the solutions in a prototype called AR= GUS, and conducts experimental evaluations on the implemented techniques. Incremental query evaluation is realized by a set of algorithms based on materializing intermediate results to incrementally evaluate selections/joins (Rete), aggregates (incremental aggregation), and set operators (incremental set operations). The query optimization techniques include transitivity inference to derive highly selective predicates, conditional materialization to selectively materialize intermediate results, join order optimization to reduce join computations, and minimum column projection to project only necessary columns. Computation sharing is realized by an incremental multiple query optimization (IMQO) approach for tractable plan construction and dynamic query registration. It applies four steps to register a new query Q, recording existing query computations of the multi-query plan R, searching common computations between Q and R, selecting optimal sharing paths, and adding new computations to obtain final results for Q and R. The thesis presents a comprehensive computation indexing and searching scheme, and presents several sharing strategies. Finally, the evaluations on two data sets show that each technique leads to significant improvement in system performance up to hundreds-fold speed-up. ARGUS is implemented atop a widely used commercial DBMS Oracle to allow fast deployment of the prototype as a value-added package to existing database applications where requirements of stream processing are growing rapidly in both scale and diversity. Future work includes supporting adaptive query processing, supporting distributive and parallel computing, and execution optimization.

Cited by 1

Optimizing Multiple Continuous Queries

Chun Jin · 2006

Emerging data stream processing applications present new challenges that are not addressed by traditional DBMS technologies. To provide practical solutions for matching highly dynamic data streams with multiple long-lived and dynamically-updated continuous queries, a stream processing system should support incremental evaluation over new data, query optimization for continuous queries including computation sharing among multiple queries. This thesis addresses these problems, presents the solutions in a prototype called AR-GUS, and conducts experimental evaluations on the implemented techniques. Incremental query evaluation is realized by a set of algorithms based on materializing intermediate results to incrementally evaluate selections/joins (Rete), aggregates (incremental aggregation), and set operators (incremental set operations). The query optimization techniques include transitivity inference to derive highly selective predicates, conditional materialization

Cited by 0

Answering Continuous Description Logic Queries

Carlos Bobed, Fernando Bobillo, Sergio Ilarri et al. · International Journal on Semantic Web and Information Systems · 2014

During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.

Cited by 10

Continuous Queries over Data Streams

Shivnath Babu, et al. · 2008

In many recent applications, data may take the form of continuous data streams, rather than finite stored data sets. Several aspects of data management need to be reconsidered in the presence of data streams, offering a new research direction for the database community. In this paper we focus primarily on the problem of query processing, specifically on how to define and evaluate continuous queries over data streams. We address semantic issues as well as efficiency concerns. Our main contributions are threefold. First, we specify a general and flexible architecture for query processing in the presence of data streams. Second, we use our basic architecture as a tool to clarify alternative semantics and processing techniques for continuous queries. The architecture also captures most previous work on continuous queries and data streams, as well as related concepts such as triggers and materialized views. Finally, we map out research topics in the area of query processing over data streams, showing where previous work is relevant and describing problems yet to be addressed.

Cited by 0

Continuous queries query-item similarity

2016

Approach: Continuous top-k text queries News and social media produce huge amounts of information items Millions of users want to receive personalized views over these streams Feedback signals (events) on items (clicks, favorite/like, etc.), arriving at high rates, provide valuable information for ranking Challenge: Efficiently solve the personalized ranking and filtering problem in this large scale real-time application scenario Users define full text queries and receive the top-k matching items Scoring function:

Cited by 0

Continuous visible nearest neighbor queries

Yunjun Gao, Baihua Zheng, Wang-Chien Lee et al. · 2009

In this paper, we identify and solve a new type of spatial queries, called continuous visible nearest neighbor (CVNN) search. Given a data set P, an obstacle set O, and a query line segment q, a CVNN query returns a set of (p, R) tuples such that p ? P is the nearest neighbor (NN) to every point r along the interval R ? q as well as p is visible to r. Note that p may be NULL, meaning that all points in P are invisible to all points in R, due to the obstruction of some obstacles in O. In this paper, we formulate the problem and propose efficient algorithms for CVNN query processing, assuming that both P and O are indexed by R-trees. In addition, we extend our techniques to several variations of the CVNN query. Extensive experiments verify the efficiency and effectiveness of our proposed algorithms using both real and synthetic datasets.

Cited by 44

Adaptive Caching for Continuous Queries

Shivnath Babu, Kamesh Munagala, Jennifer Widom et al. · 2005

We address the problem of executing continuous multiway join queries in unpredictable and volatile environments. Our query class captures windowed join queries in data stream systems as well as conventional maintenance of materialized join views. Our adaptive approach handles streams of updates whose rates and data characteristics may change over time, as well as changes in system conditions such as memory availability. In this paper we focus specifically on the problem of adaptive placement and removal of caches to optimize join performance. Our approach automatically considers conventional tree-shaped join plans with materialized subresults at every intermediate node, sub result-free MJoins, and the entire spectrum between them. We provide algorithms for selecting caches, monitoring their cost and benefits in current conditions, allocating memory to caches, and adapting as conditions change. All of our algorithms are implemented in the STREAM prototype data stream management system and a thorough experimental evaluation is included.

Cited by 99