Distributed Database Optimization on HelthCare

Distributed Database Optimization on HelthCare

Abstract:-

MODERN information processing is moving into a realm where we often need to process data that are pushed or pulled from autonomous data sources through heterogeneous networks. Adaptive join algorithms have recently attracted a lot of attention in emerging applications where data are provided by autonomous data sources through heterogeneous network environments

Their main advantage over traditional join techniques is that they can start producing join results as soon as the first input tuples are available, thus, improving pipelining by smoothing join result production and by masking source or network delays.

In this project, we first propose Double Index NEsted-loops Reactive join (DINER), a new adaptive two-way join algorithm for result rate maximization. DINER combines two key elements: an intuitive flushing policy that aims to increase the productivity of in-memory tuples in producing results during the online phase of the join, and a novel reentrant join technique that allows the algorithm to rapidly switch between processing in-memory and disk-resident tuples, thus, better exploiting temporary delays when new data are not available

We then extend the applicability of the proposed technique for a more challenging setup: handling more than two inputs. Multiple Index NEsted-loop Reactive join (MINER) is a multiway join operator that inherits its principles from DINER. Our experiments using real and synthetic data sets demonstrate that DINER outperforms previous adaptive join algorithms in producing result tuples at a significantly higher rate, while making better use of the available memory. Our experiments also shows that in the presence of multiple inputs, MINER manages to produce a high percentage of early results

Existing System:-

All existing algorithms work in three stages. During the Arriving phase, a newly arrived tuple is stored in memory and it is matched against memory-resident tuples belonging to the other relations participating in the join. Since the allocated memory for the join operation is limited and often much smaller than the volume of the incoming data, this results in tuple migration to disk. The decision on what to flush to disk influences significantly the number of results produced during the Arriving phase. The Arriving phase is suspended when all data sources are temporarily blocked and a Reactive phase kicks in and starts joining part of the tuples that have been flushed to disk. An important desideratum of this phase is the prompt handover to the Arriving phase as soon as any of the data sources restarts sending tuples. Each algorithm has a handover delay which depends on the minimum unit of work that needs to be completed before switching phases. This delay has not received attention in the past, but we show that it can easily lead to input buffer overflow, lost tuples, and hence incorrect results. When all sources complete the data transmission, a Cleanup phase is activated and the tuples that were not joined in the previous phases (due to flushing of tuples to disk) are brought from disk and joined. Even if the overall strategy has been proposed for a multiway join, most existing algorithms are limited to a two-way join. Devising an effective multiway adaptive join operator is a challenge in which little progress has been mad.

Proposed System:-

In this project, we propose two new adaptive join algorithms for output rate maximization in data processing over autonomous distributed sources. The first algorithm, Double Index NEsted-loop Reactive join (DINER) is applicable for two inputs, while Multiple Index NEsted-loop Reactive join (MINER) can be used for joining an arbitrary number of
input sources.

SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
System: Pentium IV 2.4 GHz
Hard Disk: 40GB
Ram: 512 MB
SOFTWARE REQUIREMENTS:
Microsoft visual studio 2008(ASP.NET,c#)
SQL server 2005

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