TECHNIQUES FOR OPTIMIZING THE QUERY PERFORMANCE OF DISTRIBUTED XML DATABASE - NAHID NEGAR

PROBLEM STATEMENT EXPLORING THE RESEARCH SCOPE FOR IMPROVING THE PERFORMANCE OF THE DISTRIBUTED QUERY PROCESS FOR XML DATABASE. THE RESEARCH PAPER DESCRIBES: THE ISSUES AND CONSIDERATIONS FOR DISTRIBUTED XML QUERY PROCESSING. EXPLORING CLASSICAL QUERY OPTIMIZATION TECHNIQUES PRESENTING SIMILAR RESEARCH WORK DONE BY OTHERS. ANALYZED THE RESEARCH SCOPE AND DIRECTIONS.

DISTRIBUTED XML DATABASE XML FILES ARE IDEAL FOR DESCRIBING SEMI STRUCTURED DATA. WITH THE INCREASE AMOUNT OF DATA, THE XML DATABASES ARE EXPANDED [1][1] STORAGE OF A LARGE NUMBER OF XML FILES PRESERVING THE HIERARCHICAL FORMAT. DATA IS DISTRIBUTED OR FRAGMENTED IN DIFFERENT LOCATIONS, CAN BE EVEN DIFFERENT GEOGRAPHIC LOCATION. DATA INTEGRATION IS NEEDED WHEN PROCESSING A QUERY ON DISTRIBUTED DATABASE [2].[2]

WHY DISTRIBUTED XML DATABASE IS NEEDED [6][6] LOWER COSTS INCREASED SCALABILITY INCREASED AVAILABILITY DISTRIBUTION OF SOFTWARE MODULES NEW APPLICATIONS BASED ON DISTRIBUTION MARKET FORCES

XML DATABASE AND QUERY PROCESSING XML DDL – DTD XML SCHEMA - XSD XML DML XML QUERY LANGUAGES (EXAMPLE XQUERY) ATTRIBUTES OF XML DATABASE: MULTIPLE LEVELS OF VALIDITY ENTITIES AND URI TRANSFORMATIONS

DISTRIBUTED XML QUERY PROCESSING CONSIDERATIONS [7][7] ARCHITECTURE OF DISTRIBUTED QUERY PROCESSING SYSTEMS CENTRALIZED VS. DISTRIBUTED PROCESSING OF DISTRIBUTED QUERY STATIC VS. DYNAMIC QUERY PROCESSING DATA VS. QUERY SHIPPING

DISTRIBUTED XML QUERY PROCESSING ISSUES [7][7] DIFFERENT QUERY PROCESSING CAPABILITIES OF THE DATA SOURCES UNAVAILABILITY OF STATISTICAL INFORMATION ON THE DATA SOURCES UNRELIABLE RESPONSE TIMES DATA REDUNDANCY TIME TO LAST VS. TIME TO FIRST ELEMENT

POPULAR PERFORMANCE IMPROVEMENT TECHNIQUE FOR DISTRIBUTED XML QUERY [6][6] SELECTIVITY: FACILITATE QUERY PLANNER WITH ABILITY OF SELECTIVITY ESTIMATION SELECTION PUSHDOWN: PERFORM SELECTIONS AS SOON AS POSSIBLE IN THE QUERY TREE INCREMENTAL UPDATES: THE MATERIALIZED VIEW IS UPDATED TO REFLECT THE CHANGES VIEW QUERYING: QUERIES CAN BENEFIT FROM EXPLOITING EXISTING MATERIALIZED VIEWS QUERY CONTAINMENT: FIND THE COMMON SUB-QUERIES AND EXECUTE THOSE JUST ONCE

APPROACHES TAKEN BY OTHERS AN OPTIMIZING QUERY PROCESSING WITH AN EFFECTIVE CACHING MECHANISM FOR DISTRIBUTED DATABASE [5][5] EFFICIENTLY PROCESSING XML QUERIES OVER FRAGMENTED REPOSITORIES WITH PARTIX [8][8] A METHODOLOGY FOR QUERY PROCESSING OVER DISTRIBUTED XML DATABASES [4][4] SCALABLE AND DISTRIBUTED PROCESSING OF SCIENTIFIC XML DATA [3][3]

AN OPTIMIZING QUERY PROCESSING WITH AN EFFECTIVE CACHING MECHANISM FOR DISTRIBUTED DATABASE [5][5] DATABASE OPTIMIZATION FRAMEWORK HAS BEEN DESCRIBED. THE SQL STATEMENT CONTAINS ELEMENTS WHICH IS ACCEPTED BY AN XML ORIENTED COMMON DATA. A HISTORICAL DATABASE AND QUERY BASED CACHE REPLACEMENT HAS BEEN USED. AN XML DATABASE SYSTEM IS SUITABLE FOR THE IMPLEMENTATION OF DATA ANALYSIS APPLICATION. A COMMON OPTIMIZATION QUERY PROCESSING MODEL IS ALSO USED.

EFFICIENTLY PROCESSING XML QUERIES OVER FRAGMENTED REPOSITORIES WITH PARTIX [8][8] THE DATA VOLUME OF XML REPOSITORIES AND THE RESPONSE TIME OF QUERY PROCESSING HAVE BECOME AS CRITICAL ISSUES. THE TRADITIONAL FRAGMENTATION DEFINITIONS DON NOT DIRECTLY USE FOR XML DOCUMENTS. HIGH PERFORMANCE OF XML DATA SERVERS IS FOCUSED. PATRIX IS USED FOR EXPERIMENT.

A METHODOLOGY FOR QUERY PROCESSING OVER DISTRIBUTED XML DATABASES [4][4] THE METHODOLOGY FOR XQUERY QUERY PROCESSING OVER DISTRIBUTED XML DATABASES. THE TECHNIQUE CAN BE USED IN AN XML DATABASE WHICH ALLOWS FRAGMENTATION AND HOMOGENEOUS XML DATABASES. AN ARCHITECTURE BASED MEDIATOR WITH ADAPTORS ATTACHED TO REMOTE DATABASES IS PROPOSED. THREE TYPES OF FRAGMENTATION SUCH AS HORIZONTAL, VERTICAL AND HYBRID WERE USED FOR SEVERAL EXPERIMENTS.

SCALABLE AND DISTRIBUTED PROCESSING OF SCIENTIFIC XML DATA [3][3] THE BIG DATA TECHNIQUE IN XML METADATA INDEXING FOR DISTRIBUTED XML DATABASE. THE MAPREDUCE PROCESSING IS INCORPORATED. THE DATASET PROCESSING IS A CRITICAL TO ENSURE EFFECTIVE USE. AN AUTOMATED PROCESS CAN BE HELPFUL. THIS PAPER TESTED THE PERFORMANCE RESULTS USING TWO MAPREDUCE IMPLEMENTATIONS, APACHE HADOOP AND LEMO-MR.

RESEARCH SCOPE IN DISTRIBUTED XML QUERY PROCESSING PERFORMANCE STRUCTURED-NESS – HOW TO DETERMINE THE STRUCTURE AND THE INDEXES. SCHEMA HETEROGENEITY – HOW TO INTEGRATE HETEROGENEOUS SCHEMA. RELATION DEFINITION – HOW TO DEFINE RELATIONS AND COMPARISON BETWEEN XML ELEMENTS DATA SOURCE PROCESSING POWER - HOW TO DO DISTRIBUTED QUERY PROCESSING PLANNING ANSWER QUALITY – HOW TO PRODUCE AND VERIFY THE BEST RESULT. ANSWERING SPEED – HOW TO KEEP DB STATISTICS AND IMPROVE OPERATIONS. DATA SOURCE AND USER QUANTITY – PARALLEL QUERY PROCESSING ALGORITHM.

CONCLUSION XML IS A HIGHLY ACCEPTABLE FORMAT TO STORE DATA AND IS WIDELY USED WITH THE LARGE AMOUNT OF DATA PRODUCED FROM DIFFERENT LOCATION, A DISTRIBUTED XML DATABASE IS OFTEN USED. IT IS IMPORTANT TO MAINTAIN A REASONABLE PERFORMANCE FOR QUERY PROCESSING IN DISTRIBUTED DATABASE. THE GOAL OF THE PAPER IS TO, IDENTIFY THE RESEARCH SCOPE FOR DISTRIBUTED XML QUERY PROCESSING PERFORMANCE IMPROVEMENT.

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