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About 10,000 papers for “Database query”

Cloud database query

Jinsheng Li · CSUN ScholarWorks (California State University, Northridge) · 2014

Cited by 0Open access

Multimodal database query

Nicholas J. Haddock · 1992

The paper proposes a multimodal interface for a real sales database application. We show how natural language processing may be integrated with a visual, direct manipulation method of database query, to produce a user interface which supports a flexible form of query specification, provides implicit guidance about the coverage of the linguistic component, and allows more focused discourse reference.

Cited by 5Open access

Database Query Tracking

Alexander John · 2012

Describes the database tracking and query counting diagnostic functions included in Camino CMS.

Cited by 0

Database Query Navigation

Bernd Amann, Micheł Scholl · HAL (Le Centre pour la Communication Scientifique Directe) · 1993

National audience

Cited by 1

Database Query and Reasoning

Gowtham Naidu Y · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · 2025

The rapid evolution of data-driven decision-making has highlighted the need for systems capable of seamlessly bridging the gap between natural human language and structured query languages like SQL. Traditional database systems often demand a technical understanding of SQL syntax and database schemas, creating significant barriers for non-technical users. To address this challenge, the "Database Query and Reasoning" project introduces an intelligent system designed to interpret natural language inputs, convert them into accurate SQL queries, and deliver both query results and explanatory reasoning for the outputs. This approach empowers users, regardless of technical expertise, to interact with complex databases effectively and derive actionable insights. Keywords: Natural Language Processing (NLP),AI Model Integration, CSV-to-Database Conversion, Reasoning Module,Query Result Visualization

Cited by 0Open access

Advanced Database Query Systems

Yan, Li 1964-, Ma, Zongmin 1965- · Advances in data mining and database management book series · 2011

"This book focuses on technologies and methodologies of database queries, XML and metadata queries, and applications of database query systems, aiming at providing a single account of technologies and practices in advanced database query systems" - Provided by publisher

Cited by 8

In‐Memory Database Query

Iason Giannopoulos, Abhairaj Singh, Manuel Le Gallo et al. · Advanced Intelligent Systems · 2020

In recent years, several in‐memory logic primitives were proposed where bit‐wise logical operations are performed in memory by exploiting the physical attributes of memristive devices organized in a crossbar array. However, a convincing real‐world application for in‐memory logic and its experimental validation are still lacking. Herein, the application of database query where a database is stored in an array of binary memristive devices is presented. The queries are formulated in terms of bulk bit‐wise operations and are executed in memory by exploiting Kirchhoff's current summation law. The concept is experimentally demonstrated by executing error‐free queries on a small 4 × 8 selector‐less phase‐change memory crossbar. The impact of crossbar size, resistance of routing wires, and interdevice variability on the accuracy of the logical operations are studied through numerical and circuit‐level simulations. Finally, a system for cascaded query is proposed that combines the in‐memory logic with conventional digital logic and its functionality is verified on a healthcare‐related database. It is estimated that an 11‐step long query is executed in 36 ns, consuming 560 μW, thus achieving an energy efficiency of 166 TOPS/W.

Cited by 29Open access

Statistical Database Query Languages

Gültekin Özsoyoğlu, Z. Meral Özsoyoğlu · IEEE Transactions on Software Engineering · 1985

Databases that are mainly used for statistical analysis are called statistical databases (SDB). A statistical database management system (SDBMS) may be defined as a database management system that provides capabilities 1) to model, store, and manipulate data in a manner suitable for the needs of SDB users, and 2) to apply statistical data analysis techniques that range from simple summary statistics to advanced procedures. This paper surveys the existing and proposed SDB data definition and data manipulation (i.e., query) languages.

Cited by 42

Graph database and query.

Rosalba Giugno (46523), Vincenzo Bonnici (475167), Nicola Bombieri (475168) et al. · Figshare · 2015

<p>The database is composed by four graphs , , and . is the query. The occurrences of in the graphs are shown by colored vertices and bold edges.</p>

Cited by 0Open access

Intelligent database query system

G.Y. Show · DR-NTU (Nanyang Technological University) · 1992

Natural language communication with computers has long been a major goal of AI both for the information it can give about intelligence in general and for its practical utility. Databases, software packages and Al-based expert systems all require flexible interfaces to a growing community of users who are not able or do not wish to communicate with computer in formal, artificial command languages. Whereas many of the fundamental problems of general natural language processing (NLP) by machine remain to be solved, the area has mature in recent years to the point that practical natural language interface to software systems can be developed in many restricted, but nevertheless useful circumstances.

Cited by 0

Database query for Panurus Biarmicus Database: survival_MSData

Lendvai, Ádám · OpenBioMaps Consortium · 2020

The current dataset contains information about the effects of an experimental manipulation of IGF-1 on survival and oxidative damage of bearded reedlings (Panurus biarmicus). Results of this study are reported in https://doi.org/10.1101/2020.03.27.012120

Cited by 0Open access

Design Enrichment of Query Forms for Database Query

R. S. Bhalerao, Dhananjay Kumbhakarna, Ashvini Avhad et al. · Asian Journal of Computer Science and Technology · 2016

The scientific databases & web databases maintain huge and large amount of data. The real-world databases contain over thousands of relations & attributes. predefined database query forms are not able to satisfy various queries from users on those databases. The review of DQF is to capture a user’s preference and rating query form components, assisting to take decisions. The creation of a query form is an faster process and is given by the user. A user can also create the query form and submit queries to view the query output at each iteration. This way, a query form could be dynamically created till the user satisfies with the query forms. The important F-measure for measuring the goodness of a query form. A model is developed for estimating the goodness of a query form in DQF. Experimental evaluation and user study demonstrate the accuracy and performance of the system. The ranking of form components is based on the captured user preference. A user can also fill the query form and submit queries to view the query output at each step. This type a query form could be dynamically refined till the user satisfies with the query results.

Cited by 0Open access

The database query language FAQUEL

Polster, F.J., Tritschler, P., Kernforschungszentrum Karlsruhe G.m.b.H. (Germany, F.R.). Inst. fuer Datenverarbeitung in der Technik · OpenGrey (Institut de l'Information Scientifique et Technique) · 1981

Cited by 0

Architecture-sensitive database query processing

Kenneth A. Ross, Jingren Zhou · 2004

During the last decade, microprocessors have experienced tremendous improvement. This architectural growth has not been equally distributed over all aspects of hardware performance. Recent advances in the speed of commodity CPUs have far outpaced advances in memory latency. Main memory access is therefore becoming a significant cost component of database operations. Database systems face new performance bottlenecks, such as memory access and poor utilization of sophisticated execution hardware. Research has shown that the DBMS hardware behavior is suboptimal, compared with scientific workloads. This illustrates the importance of rethinking and developing database query processing algorithms in the context of new computer architectures. This thesis focuses on studying interactions between DBMMs and modern processor architecture. We design techniques to exploit architectural innovations and to alleviate performance bottlenecks throughout the CPU and the memory hierarchy, including caches, memory, and disks. The first part of this thesis presents a novel technique to implement database operations with a high degree of intra-instruction parallelism. We describe new SIMD instructions commonly available in commodity processors and show how database operations can be accelerated using SIMD instructions. Database research has demonstrated that the dominant memory stalls are due to the data cache misses on the second-level cache and the instruction cache misses on the first-level instruction cache. We address both issues in the second part of this thesis. We propose buffering techniques to improve the data cache performance of index structures and to improve the instruction cache performance of query processing in database systems. The main benefit derives from better data and instruction reference locality. Our techniques can be easily integrated into current database systems without significant changes. The final part of this thesis describes a new storage model called MBSM (Multi-resolution Block Storage Model) for laying out tables on disks. Compared to other data storage models, MBSM has both good I/O performance and good cache utilization in main-memory.

Cited by 0

Query optimization in database systems

Çiğdem Kaptanoğlu · Istanbul Technical University Academic Open Archive (Istanbul Technical University) · 1992

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Fen Bilimleri Enstitüsü, 1992

Cited by 0

Advanced Database Aggregation Query Processing

Donghui Zhang · 2007

Abstract. The aggregation query is an important but costly operation in database management systems. In the worst case, to compute an aggregate, all tuples in a database needs to be examined. However, in many applications (on-line analysis, etc.), we need to compute the aggregates very fast. In the Ph.D. research, we focus on devising specialized indices for the box-aggregation query and its relatives. Our research shows that the newly designed structures have much better query performance than the existing solutions, sometimes over a hundred times faster. In this paper, we summarize our findings. 1

Cited by 0