Constraint database query evaluation with approximation

ABSTRACT In this case, students assume the role of an accounting professional asked by a client to investigate why net income is not as strong as expected. The students must first analyze a set of financial statements to identify areas of possible concern. After determining the areas to investigate, the students use a database query tool to see if they can determine causes by examining transaction level data. Finally, the students are asked to professionally communicate their findings and recommendations to their client. The case provides students with experience in using query-based approaches to answering business questions. It is appropriate for students with basic query and financial analysis skills and knowledge of internal controls. A Microsoft Access database with transaction details for the final seven months of the current year as well as financial statements for the current and prior year are provided.

Download Full-text

Visualization of Database Query Improved by Fuzzy Logic Using Virtual Reality

10.21528/cbic2011-05.1 ◽

2016 ◽

Author(s):

Vanessa Avelino Xavier de Camargo ◽

Marcos Wagner de Souza Ribeiro

Keyword(s):

Virtual Reality ◽

Fuzzy Logic ◽

Database Query

Download Full-text

Query Rewriting for Incremental Continuous Query Evaluation in HIFUN

Algorithms ◽

10.3390/a14050149 ◽

2021 ◽

Vol 14 (5) ◽

pp. 149

Author(s):

Petros Zervoudakis ◽

Haridimos Kondylakis ◽

Nicolas Spyratos ◽

Dimitris Plexousakis

Keyword(s):

Query Optimization ◽

Query Language ◽

Computational Cost ◽

Continuous Queries ◽

Continuous Query ◽

Query Rewriting ◽

Query Evaluation ◽

Clear Separation ◽

Complete Dataset ◽

High Level

HIFUN is a high-level query language for expressing analytic queries of big datasets, offering a clear separation between the conceptual layer, where analytic queries are defined independently of the nature and location of data, and the physical layer, where queries are evaluated. In this paper, we present a methodology based on the HIFUN language, and the corresponding algorithms for the incremental evaluation of continuous queries. In essence, our approach is able to process the most recent data batch by exploiting already computed information, without requiring the evaluation of the query over the complete dataset. We present the generic algorithm which we translated to both SQL and MapReduce using SPARK; it implements various query rewriting methods. We demonstrate the effectiveness of our approach in temrs of query answering efficiency. Finally, we show that by exploiting the formal query rewriting methods of HIFUN, we can further reduce the computational cost, adding another layer of query optimization to our implementation.

Download Full-text

A comparison of abstract data type and constraint database approaches to GIS query languages

Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems - GIS '09 ◽

10.1145/1653771.1653824 ◽

2009 ◽

Author(s):

Peter Revesz

Keyword(s):

Query Languages ◽

Data Type ◽

Abstract Data Type ◽

Abstract Data ◽

Constraint Database

Download Full-text

Age-Aware Query Evaluation for Big Data Analytics in Mobile Edge Clouds

2020 IEEE 22nd International Conference on High Performance Computing and Communications; IEEE 18th International Conference on Smart City; IEEE 6th International Conference on Data Science and Systems (HPCC/SmartCity/DSS) ◽

10.1109/hpcc-smartcity-dss50907.2020.00027 ◽

2020 ◽

Author(s):

Qiufen Xia ◽

Wenhao Ren ◽

Mingchu Li ◽

Jiankang Ren

Keyword(s):

Big Data ◽

Data Analytics ◽

Big Data Analytics ◽

Query Evaluation

Download Full-text

Technical Perspective

ACM SIGMOD Record ◽

10.1145/3471485.3471499 ◽

2021 ◽

Vol 50 (1) ◽

pp. 59-59

Author(s):

Marcin Zukowski

Keyword(s):

Query Processing ◽

Hash Tables ◽

Database Query ◽

Query Performance ◽

Research Opportunities ◽

Database Query Processing

Hash tables are possibly the single most researched element of the database query processing layers. There are many good reasons for that. They are critical for some key operations like joins and aggregation, and as such are one of the largest contributors to the overall query performance. Their efficiency is heavily impacted by variations of workloads, hardware and implementation, leading to many research opportunities. At the same time, they are sufficiently small and local in scope, allowing a starting researcher, or even a student, to understand them and contribute novel ideas. And benchmark them. . . Oh, the benchmarks. . . :)

Download Full-text