A Histogram Based Analytical Approximate Query Processing for Massive Data

In this paper, we study the characteristics of analytical query processing and proposed a histogram based approximate method for query processing over massive data. We implemented this approach into Hive system and evaluate it with Hive and BlinkDB cluster, the experimental results verified that our method is significantly fast than these existing techniques.

Towards crowd-aware indoor path planning

Proceedings of the VLDB Endowment ◽

10.14778/3457390.3457401 ◽

2021 ◽

Vol 14 (8) ◽

pp. 1365-1377

Author(s):

Tiantian Liu ◽

Huan Li ◽

Hua Lu ◽

Muhammad Aamir Cheema ◽

Lidan Shou

Keyword(s):

Path Planning ◽

Query Processing ◽

Travel Time ◽

Real Data ◽

Experimental Results ◽

Search Process ◽

Unified Framework ◽

Approximate Query ◽

Processing Algorithms

Indoor venues accommodate many people who collectively form crowds. Such crowds in turn influence people's routing choices, e.g., people may prefer to avoid crowded rooms when walking from A to B. This paper studies two types of crowd-aware indoor path planning queries. The Indoor Crowd-Aware Fastest Path Query (FPQ) finds a path with the shortest travel time in the presence of crowds, whereas the Indoor Least Crowded Path Query (LCPQ) finds a path encountering the least objects en route. To process the queries, we design a unified framework with three major components. First, an indoor crowd model organizes indoor topology and captures object flows between rooms. Second, a time-evolving population estimator derives room populations for a future timestamp to support crowd-aware routing cost computations in query processing. Third, two exact and two approximate query processing algorithms process each type of query. All algorithms are based on graph traversal over the indoor crowd model and use the same search framework with different strategies of updating the populations during the search process. All proposals are evaluated experimentally on synthetic and real data. The experimental results demonstrate the efficiency and scalability of our framework and query processing algorithms.

Monotone Approximate Query Processing

10.21236/ada267153 ◽

1992 ◽

Author(s):

Jane Liu

Keyword(s):

Query Processing ◽

Approximate Query Processing for Big Data in Heterogeneous Databases

2020 IEEE International Conference on Big Data (Big Data) ◽

10.1109/bigdata50022.2020.9378310 ◽

2020 ◽

Author(s):

Manoj Muniswamaiah ◽

Tilak Agerwala ◽

Charles C. Tappert

Keyword(s):

Big Data ◽

Query Processing ◽

Heterogeneous Databases ◽

Efficient Approximate Query Processing in Peer-to-Peer Networks

IEEE Transactions on Knowledge and Data Engineering ◽

10.1109/tkde.2007.1064 ◽

2007 ◽

Vol 19 (7) ◽

pp. 919-933 ◽

Cited By ~ 10

Author(s):

Benjamin Arai ◽

Gautam Das ◽

Dimitrios Gunopulos ◽

Vana Kalogeraki

Keyword(s):

Query Processing ◽

Peer To Peer ◽

Peer Networks ◽

Peer To Peer Networks ◽

Concurrency Control for Approximate Query Processing of Real-Time Database Systems

Real-Time Database and Information Systems: Research Advances ◽

10.1007/978-1-4615-6069-2_13 ◽

1997 ◽

pp. 227-246 ◽

Cited By ~ 1

Author(s):

Susan V. Vrbsky ◽

Saša Tomić ◽

Nenad Jukić

Keyword(s):

Query Processing ◽

Real Time ◽

Concurrency Control ◽

Database Systems ◽

Approximate Query ◽

Real Time Database

Approximate Query Processing Model for Mobile Computing

Information Organization and Databases ◽

10.1007/978-1-4615-1379-7_15 ◽

2000 ◽

pp. 207-219

Author(s):

Sanjay Kumar Madria ◽

Mukesh Mohania ◽

John F. Roddick

Keyword(s):

Mobile Computing ◽

Query Processing ◽

Modeling Large Time Series for Efficient Approximate Query Processing

Database Systems for Advanced Applications - Lecture Notes in Computer Science ◽

10.1007/978-3-319-22324-7_16 ◽

2015 ◽

pp. 190-204 ◽

Cited By ~ 3

Author(s):

Kasun S. Perera ◽

Martin Hahmann ◽

Wolfgang Lehner ◽

Torben Bach Pedersen ◽

Christian Thomsen

Keyword(s):

Time Series ◽

Query Processing ◽

Large Time ◽

Approximate Processing for Medical Record Linking and Multidatabase Analysis

Medical Informatics ◽

10.4018/978-1-60566-050-9.ch167 ◽

2011 ◽

pp. 2203-2217

Author(s):

Qing Zhang

Keyword(s):

Query Processing ◽

Medical Record ◽

Related Data ◽

Multidatabase Systems ◽

Aggregate Queries ◽

Health Related ◽

Approximate Query ◽

Approximate Answers ◽

Query Planning

In this article we investigate how approximate query processing (AQP) can be used in medical multidatabase systems. We identify two areas where this estimation technique will be of use. First, approximate query processing can be used to preprocess medical record linking in the multidatabase. Second, approximate answers can be given for aggregate queries. In the case of multidatabase systems used to link health and health related data sources, preprocessing can be used to find records related to the same patient. This may be the first step in the linking strategy. If the aim is to gather aggregate statistics, then the approximate answers may be enough to provide the required answers. At least they may provide initial answers to encourage further investigation. This estimation may also be used for general query planning and optimization, important in multidatabase systems. In this article we propose two techniques for the estimation. These techniques enable synopses of component local databases to be precalculated and then used for obtaining approximate results for linking records and for aggregate queries. The synopses are constructed with restrictions on the storage space. We report on experiments which show that good approximate results can be obtained in a much shorter time than performing the exact query.