Query Planning for
Continuous Aggregation Queries over a Network of Data Aggregators
ABSTRACT:
Continuous queries are used to monitor
changes to time varying data and to provide results useful for online decision making.
Typically a user desires to obtain the value of some aggregation function over
distributed data items, for example, to know value of portfolio for a client;
or the AVG of temperatures sensed by a set of sensors. In these queries a
client specifies a coherency requirement as part of the query. We present a
low-cost, scalable technique to answer continuous aggregation queries using a
network of aggregators of dynamic data items. In such a network of data
aggregators, each data aggregator serves a set of data items at specific
coherencies. Just as various fragments of a dynamic webpage are served by one
or more nodes of a content distribution network, our technique involves
decomposing a client query into sub-queries and executing sub-queries on
judiciously chosen data aggregators with their individual sub-query incoherency
bounds. We provide a technique for getting the optimal set of sub-queries with their
incoherency bounds which satisfies client query’s coherency requirement with
least number of refresh messages sent from aggregators to the client. For
estimating the number of refresh messages, we build a query cost model which
can be used to estimate the number of messages required to satisfy the client
specified incoherency bound. Performance results using real-world traces show that
our cost-based query planning leads to queries being executed using less than
one third the number of messages required by existing schemes.
SYSTEM MODELS
HARDWARE REQUIREMENT
CPU type
: Intel Pentium 4
Clock speed :
3.0 GHz
Ram size
: 512 MB
Hard disk capacity : 40 GB
Monitor type
: 15 Inch color monitor
Keyboard type : internet keyboard
Mobile
: ANDROID MOBILE
SOFTWARE REQUIREMENT
Operating System: Android
Language :
ANDROID SDK 2.3
Documentation : Ms-Office
REFERENCE:
Rajeev Gupta and Krithi Ramamritham,
“Query Planning for Continuous Aggregation Queries over a Network of Data
Aggregators”, IEEE TRANSACTIONS ON
KNOWLEDGE AND DATA ENGINEERING, VOL. 24, NO.6, JUNE 2012.
