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About 1,280 papers for “query decomposition”
Marcin Sydow, Francesco Bonchi, Carlos Castillo et al. · 2009
Topical query decomposition (TQD) is a paradigm recently introduced in [1], which, given a query, returns to the user a set of queries that cover the answer set of the original query. The TQD problem was studied as a variant of the set-cover problem and solved by means of a greedy algorithm.
Cited by 3
Christos Nomikos, Manolis Gergatsoulis, Eleftherios Kalogeros et al. · 2014
Cited by 0
Danushka Bollegala, Tomoya Machide, Ken‐ichi Kawarabayashi · arXiv (Cornell University) · 2019
We propose a method to protect the privacy of search engine users by decomposing the queries using semantically \emph{related} and unrelated \emph{distractor} terms. Instead of a single query, the search engine receives multiple decomposed query terms. Next, we reconstruct the search results relevant to the original query term by aggregating the search results retrieved for the decomposed query terms. We show that the word embeddings learnt using a distributed representation learning method can be used to find semantically related and distractor query terms. We derive the relationship between the \emph{obfuscity} achieved through the proposed query anonymisation method and the \emph{reconstructability} of the original search results using the decomposed queries. We analytically study the risk of discovering the search engine users' information intents under the proposed query obfuscation method, and empirically evaluate its robustness against clustering-based attacks. Our experimental results show that the proposed method can accurately reconstruct the search results for user queries, without compromising the privacy of the search engine users.
Cited by 0Open access
Sebastián Alberto Grillo, Franklin de Lima Marquezino · Theoretical Computer Science · 2018
Cited by 7Open access
Yong-ju Lee, Sung-jin Lee, Chin-wan Chung · 2007
Cited by 0
Roxana Petcu, Kenton Murray, Daniel Khashabi et al. · 2026
Roxana Petcu, Kenton Murray, Daniel Khashabi, Evangelos Kanoulas, Maarten de Rijke, Dawn Lawrie, Kevin Duh. Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.
Cited by 0Open access
David J. Brenes, Daniel Gayo-avello, Rodrigo Garcia · 2010
Cited by 0
Roxana Petcu, Kenton Murray, Daniel Khashabi et al. · ArXiv.org · 2025
Retrieval-augmented generation (RAG) systems address complex user requests by decomposing them into subqueries, retrieving potentially relevant documents for each, and then aggregating them to generate an answer. Efficiently selecting informative documents requires balancing a key trade-off: (i) retrieving broadly enough to capture all the relevant material, and (ii) limiting retrieval to avoid excessive noise and computational cost. We formulate query decomposition and document retrieval in an exploitation-exploration setting, where retrieving one document at a time builds a belief about the utility of a given sub-query and informs the decision to continue exploiting or exploring an alternative. We experiment with a variety of bandit learning methods and demonstrate their effectiveness in dynamically selecting the most informative sub-queries. Our main finding is that estimating document relevance using rank information and human judgments yields a 35% gain in document-level precision, 15% increase in α-nDCG, and better performance on the downstream task of long-form generation.
Cited by 0Open access
2008
Domains like bioinformatics are complex data integration domains because data from remote sources and specialized applications need to be combined to answer queries. An important characteristic of such a domain is that actions may be mutually exclusive or causally related. Moreover, there is (partially complete) domain specific knowledge about how queries should be answered since an average user is a sophisticated domain expert. Query plans in these domains should not only answer the query but also respect any user intent or domain guidance provided to improve the perceived quality of the result and query execution time. Previously, methods like rule inversion and view unfolding have been found to be more effective than AI planning in obtaining access sequences for sources without interactions, a case when sources are just repositories of data. We present a solution in SHQPlanner, a hierarchical temporal planner for query planning and execution monitoring in complex domains based on previous theoretical work on HTN planning with partial domain knowledge on one hand and temporal reasoning for query cost on the other. SHQPlanner is a sound, complete, and efficient domain-independent query planner that can incorporate partial query decomposition, source preferences, data and application interaction, and temporal constraints.
Cited by 0
Sena Nural, Pinar Koksal, Fatma Ozcan et al. · 1996
A multidatabase system allows its users to simultaneously access heterogeneous, and autonomous databases using an integrated schema and a single global query language. An important problem in multidatabase systems is processing of the global queries. In this paper, we describe a global query processing scheme as it is implemented in a multidatabase environment, namely MIND. Since multidatabase query processing is very much dependent on the way schema integration is realized, the underlying MIND schema integration is also described. The details of both query decomposition process and the necessary post-processing to combine the partial results coming from local databases, are provided. 1 INTRODUCTION Various types of database systems are currently in use today. These information resources could be immediately made available for many users through existing computer systems. However, since these systems often use different data models and different query languages, users of one system ca...
Cited by 0
Zuofu Zhu, Chao Xu, Hongmei Ge · 2011
Aiming at algorithm limitations and lower recall rate of query decomposition, a query decomposition algorithm based on FWR expressions of ontology XQuery is proposed. In this algorithm, after writed query language the global by XQuery, in the three stages of the FWR expression global ontology-based query is decomposed into sub-queries for the local body. The Algorithm reduces the complexity of the query decomposition, ensures the semantic integrity, and then increases the recall rate. An illustrative example as well as the experimental results demonstrates the effectiveness of the proposed method.
Cited by 0
Akili, Samira · edoc Publication server (Humboldt University of Berlin) · 2025
Complex Event Processing (CEP) Systeme evaluieren Abfragen über hochfrequenten Ereignisströmen, um relevante Muster darin zu erkennen. In Szenarien mit verteilten Ereignisquellen, etwa in Sensornetzwerken oder IoT-Anwendungen, gewinnt CEP zunehmend an Bedeutung. Bestehende Ansätze nutzen das Optimierungspotenzial in solchen Netzwerken nur begrenzt aus. Diese Dissertation stellt Techniken zur Optimierung verteilter Mustererkennung vor, die darauf abzielen, die Netzwerkkosten, also die Anzahl der im Netzwerk ausgetauschten Nachrichten, zu minimieren. Dazu nutzen wir sowohl die Semantik der Abfragesprache als auch Netzwerkeigenschaften aus. Insbesondere führen wir eine Dekompositionstechnik von Mustern ein, mit der korrekte Teilmuster spezifiziert und für die verteilte Erkennung genutzt werden können. Darauf aufbauend führen wir Modelle für die verteilte Mustererkennung in Netzwerken ein: MuSE- und INEv-Graphen. Während MuSE-Graphen eine vollständige Netzwerktopologie voraussetzen, erlauben INEv-Graphen beliebige Netzwerktopologien und spezifizieren die effizienten Weiterleitung von Ereignissen. Zusätzlich untersuchen wir die Ereignisübertragung. Während Ereignisse üblicherweise sofort nach ihrer Generierung versandt werden, können Kommunikationskosten reduziert werden, indem die Übertragung erst durch eine Anfrage geschieht. Hierzu führen wir PrePP-Pläne ein, die die Anfrage-basierte Übertragung von Ereignissen mit Prädikat-basierter Filterung modellieren. Schließlich übertragen wir unsere Dekompositionstechniken auf die parallele Mustererkennung. Dabei analysieren wir Durchsatzengpässe bei Rechenknoten und entwickeln ein Kostenmodell zur Musterdetektion in Clusterumgebungen. Die resultierenden DecoPa-Pläne ermöglichen eine durchsatzoptimierte, parallele Mustererkennung. Für alle Modelle definieren wir formale Eigenschaften, beweisen Korrektheit und geben Konstruktionsalgorithmen an. Experimente zeigen die Überlegenheit unserer Ansätze gegenüber bestehenden Verfahren.
Cited by 0Open access
Ramez Elmasri, P. Srinivas, Gomer Thomas · 1987
Fragmentation and query decomposition are considered important issues in a distributed database environment. For a semantic data model (ECR data model), the types of fragmentation and the criteria they should follow is provided along with how global queries in a high-level language, GORDAS, are decomposed into local queries on the fragments. This technique may be used in a heterogeneous distributed database, where the global schema presented to the user is in the high-level ECR model.
Cited by 2
Association for Computational Linguistics 2025, Dorr, Bonnie, Garciga, Jairo et al. · Underline Science Inc. · 2025
Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-ofthe- art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.
Cited by 0Open access
Khan, Yasir, Wu, Xinlei, Youm, Sangpil et al. · ArXiv.org · 2025
Query-focused tabular summarization is an emerging task in table-to-text generation that synthesizes a summary response from tabular data based on user queries. Traditional transformer-based approaches face challenges due to token limitations and the complexity of reasoning over large tables. To address these challenges, we introduce DETQUS (Decomposition-Enhanced Transformers for QUery-focused Summarization), a system designed to improve summarization accuracy by leveraging tabular decomposition alongside a fine-tuned encoder-decoder model. DETQUS employs a large language model to selectively reduce table size, retaining only query-relevant columns while preserving essential information. This strategy enables more efficient processing of large tables and enhances summary quality. Our approach, equipped with table-based QA model Omnitab, achieves a ROUGE-L score of 0.4437, outperforming the previous state-of-the-art REFACTOR model (ROUGE-L: 0.422). These results highlight DETQUS as a scalable and effective solution for query-focused tabular summarization, offering a structured alternative to more complex architectures.
Cited by 0Open access
Handoko, -, Getta, Janusz R · Research Online (University of Wollongong) · 2014
Cited by 0
Samira Akili, Steven Purtzel, Matthias Weidlich · Proceedings of the ACM on Management of Data · 2024
Systems for Complex Event Processing (CEP) enable the detection of predefined patterns in event streams. While the evaluation of CEP queries is computationally hard, scalability may be achieved by parallelization. Yet, existing approaches for parallel CEP are driven by static query properties, such as partitioning keys and states of the evaluation model. They largely neglect the rates with which processing units may ingest and compare events for query evaluation. In this paper, we present an approach for parallel CEP that is based on a flexible decomposition of CEP queries. Our idea is to guide the decomposition by the sustainable throughput of each processing unit, in order to maximize the overall performance. To this end, we introduce DecoPa plans for parallel CEP, provide a cost model for them, elaborate on their correctness and optimality, and present an algorithm for their construction. Experiments using a DecoPa implementation in Flink illustrate throughput gains of up to 12 orders of magnitude compared to state-of-the-art approaches.
Cited by 5Open access
Xiantong Li, Jianzhong Li · Information Technology Journal · 2009
Cited by 0
Thomas M. Niccum, Jaideep Srivastava, Bhaskar Himatsingka et al. · 1993
In this paper we present an approach for transforming a relational join tree into a detailed execution plan with resource allocation information, for execution on a parallel machine. Our approach starts by transforming a query tree, such as might be generated by a sequential optimizer, into an operator tree which is then partitioned into a forest of linear chains of pipelined operators. We present an algorithm for scheduling these chains in conformance to their precedence ordering on a parallel machine. The aim of the scheduling is to achieve some objective like minimum response time or maximum speedup, etc. We provide a set of experiments to demonstrate the quality of the allocation plans generated by the method. A major benefit of the method is that it uses inter-operator, intra-operator and pipelining methods of parallelizing relational query operators simultaneously. Our experiments show that the heuristic scheduling method creates schedules that are close to optimal while being ve...
Cited by 0
Le Thi, Thu Thuy, Doan Dai Duong · 2005
Cited by 0