International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
Analysis of Classical Web-Centric Query
Optimization Techniques
Atul Garg
Associate Professor, MMICT & BM, MMU, Mullana, Ambala-133 207
ABSTRACT
The growth of Internet and web is changing the life style of current generation. In its result, heterogeneous data is available on
the web and to retrieve integrated data is a challenge for the researchers and developers. Query optimization techniques are used
for information retrieval. In this paper, web centric classical queries processing and optimization techniques, classification of the
different present techniques and a comprehensive framework is discussed.
Keywords: Mediator, Disco, Garlic, Hermes, Tsimmis, Infomaster, HiQIQ etc.
1. INTRODUCTION
Nowadays, Internet is playing an important role in the overall growth of the current generation. These days, people are
engaging in more and more online activities ranging from reading news, banking, banking to conducting business,
advertisement, online shopping, education, research etc. This has been particularly enabled by the technical advances
achieved in computer science. The various fields of computer science like high speed networking, communication
protocols, mark-up languages, graphical interfaces, Java technologies, and communication middleware are growing.
Earlier (in evolution period of web) the text-based web pages were provided to the users. But, as the web become more
powerful and more in demand the pressure from the user’s side is increased. The pressure was in the form of type of data,
Internet speed and Information Retrieval methods. As a result, these days, a variety of data which can be multimedia-rich
is available on the web. So, as the different type of data is available on the Internet in its result different type of
information. To retrieve different type of information the web developers are facing various challenges. Few of them are
discussed below:
 Heterogeneous Data: The data on the web is not only text but it is a combination of text, images and multimedia.
To retrieve this type of data the user sends only one query or request.
 Security Issues: The people on the Internet are doing various secure transactions like online shopping, Internet
banking etc. To retrieve or use the secure transaction the users are using the same Internet and tools.
 Performance Issues: The good performance is the key of any web site. If it is poor in performance then the users
select another site.
 Remote Servers: With the global growth of Internet the data is stored in near or far severs. So, to retrieve the data
from remote server it increases the response time and security issues.
 Decontrol: The traditional information retrieval systems were in control due to only one server and that was also in
control and in security. But now, in web-centric information retrieval system there are various servers and to
search the information the request information being moving from one server to another. So, the web-centric
queries are decontrolled after the submitting the query.
 Lager number of clients: With the revolution of electronic devices, the web users are growing day by day and
become a challenge for the web developers. Because, the developer don’t know in advance with type of device is
going to be use to retrieve the information.
Query optimization is a function of many relational database management systems in which multiple query plans for
satisfying a query are examined and a good query plan is identified [11]. It takes a central stage in data integration
systems on the web. The features of web make the task of enabling efficient querying even more difficult. The mediator
approach [8] is adopted by most of the described systems for data integration. The related systems are those that match
information requests from consumers, individuals or applications, to information providers [23].
Further, the Web’s volatility and highly dynamic nature are a challenge when the expectation is that queries always
return specific results. Also, all information sources are not expected to provide the same query capabilities. So, the
working of query processor becomes more difficult and it needs to make sure that the generated query execution plan is
feasible with respect to these limitations. In this paper, web centric classical queries processing and optimization
techniques are discussed. A classification of the different present techniques and a comprehensive framework is also
discussed. Mediator approach [5] is mostly used for data integration. The classical systems match the request from the
user or users to the host. The classical query optimization techniques are classified based on optimization techniques. The
Volume 2, Issue 12, December 2013
Page 101
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
numerous techniques like cost-based optimization techniques, adaptive query optimization, quality-based optimization,
and query optimization are discussed.
2. BACKGROUND
In this section, the pre work done by the renowned researchers is discussed. In the conventional query optimization
system a query for example in MySQL, there could be many execution plans to retrieve the results. The different methods
respond the same results. Though, the different plans are different in their performance, response time and their
parameters (resources). Different researchers are doing work on query optimization focusing on the performance, fast
accessibility and security of the web-centric quires.
Unel et al., [7] focused on query optimization techniques for multimedia databases. Further, query processing strategies
and query optimization techniques are different in multimedia and traditional databases. Researchers proposed query
optimization approaches for processing spatio-temporal queries in video database systems and implemented on a specific
system called BilVideo [7 and 19]. They applied reordering algorithms named internal node reordering and leaf node
reordering on query execution tree. Rao [29] presented sampling techniques based on selecting points from the parameter
space at random and optimizing them using a conventional optimizer. The set of plans returned was a subset of the
parametric optimal set of plans.
Ouzzani and Bouguettaya [23] discussed the difficulties of query optimization for unstructured information on the web.
The authors pointed out that querying abundant information scattered on the web raises several difficulties in achieving
efficiency. The authors surveying their research on fundamental problems to efficiently process queries over web data
integration systems were evaluated and in fact four categories of query optimization techniques i.e. cost-based, qualitybased, capability-based, and adaptive-based, in the context of web-based data integration were highlighted.
Rossi et al., [9] focused on personalized application. The researchers proposed Object-Oriented Hypermedia Design
Method (OOHDM) [9] to attract scientists to design-oriented discussion of personalization for specifying and designing
personalized behaviours in web applications. According to Liu and Ling [22] queries for the internal hierarchical
structure of web documents is more important. The researchers [22] proposed conceptual model based Web Query
Language (WebQL) [22]. WebQL is a rule-based language for querying the HTML documents for web documents.
Further, WebQL provides a simple and very powerful way to query both the structure and contents of the HTML
documents and to restructure the results. Cardellini et al., [32] considered different categories of web applications, and
evaluated how static, dynamic and secure requests affect performance and quality of service of heterogeneous information
on the web. Authors proposed a distributed web architecture that is able to guarantee the assessed service level agreement
for all client requests.
Srivastava et al., have proposed the overall goal of a general-purpose Web Service Management System (WSMS) [31],
enabling clients to query a collection of web services in a transparent and integrated approach. The authors focused on
new query optimization issues that arose in a WSMS. Their execution model consists of pipelined query processing over
web services and they derived the “bottleneck” cost metric to characterize the cost of a pipelined plan. For this cost metric,
the new algorithms not only to decide the optimal arrangements of web services in a pipelined plan respecting precedence
constraints but also to decide the optimal chunk size to use when sending data to each web service were devised. These
algorithms formed the basis of a WSMS query optimizer.
Lam and Ozsu [30] stated that high volatility of web, make querying of data difficult. A system called WebQA that
provides a declarative query-based approach to web data retrieval that uses question-answering technology in extracting
information from web sites that are retrieved by search engines was introduced. Hu and Bagga [14] focused on the idea
that the web documents are increasingly accessed from wireless devices with small screens and low bandwidths. Web
documents contain a large number of images and in fact, to identify the function of each image according to the device is
an important issue in web content repurposing. The researchers proposed to explore image-recognition techniques to
interpret images more effectively in the icon/logo category.
Cali and Calvanese [3] argued that the problem of optimizing conjunctive query answering in relational database. They
showed an effective optimization technique for pruning unnecessary accesses from query plans; their approach is
applicable in the case of full conjunctive queries that are different from literature. Meinecke et al., [13] introduced Web
Composition Architecture Model (WAM) as an overall modeling approach tailored to aspects of highly distributed
systems with federation as a related issue. Meinecke [13] and his co-researcher applied the technology used for the
functional parts of the architecture and provided a web service for querying and changing the model.
Volume 2, Issue 12, December 2013
Page 102
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
Gupta [4] et al., presented a system that matches user queries with web services operations. The research employed a selflearning mechanism and a set of algorithms and optimizations to match user queries with corresponding operations in
web services. Their system uses lexical analysis, domain-independent matching techniques, domain-specific ontologies
and a set of specialized algorithms and optimizations to match simple free-form queries. They proved that the proposed
approach is better than traditional approach in terms of effectiveness and performance.
3. ANALYSIS AND COMPARISON
In order to make use of vast amount of data, proficient techniques to access web document information based on its
content were needed to be developed. As a result, the role of Information Retrieval (IR) systems became most important.
On the whole, the most significant and difficult operations in information retrieval was to generate queries that can
succinctly identify relevant documents and reject irrelevant documents [25]. Therefore, in order to address the
prominent i.e. moving from data-centric approach to web-centric approach, more known classical web centric query
optimization techniques were discussed and are presented. A classification of the different existing techniques and a
comparison among them is highlighted in Figure 1.
Figure 1: Classification of Query Optimization
The extended query optimization techniques was already analyzed and compared in [1]. Now in classical query
optimization techniques a review of cost-based optimization techniques, adaptive query optimization, quality-based
optimization, and query optimization in presence of sources with limited capabilities is being presented.
Wiederhold [6] presented an architecture named as mediators. It was based on for some basic issues like maintenance,
problem modelling, learning and knowledge attainment. Mediators represent a schema for multiple heterogeneous and
independent information sources. Further, in this schema an interface is used for location, model and transparency. A
wrapper is used for each source to show its participation in the mediator system. Wiederhold [6] apply this schema over
a specific application domain. The working of mediator is explained in Figure 2 [6 & 23].
Figure 2: Mediator Based Architecture
3.1 Cost Based Optimization Techniques
Cost-based optimization techniques help to find or select the best efficient execution plan from the available network
paths with the help of available schemas. The basic objective this technique or approach is best throughput with
minimal resources. Information on web is in heterogeneous form. Now, to retrieve the information the basic challenge is
to optimize the query and the response information. As discussed above the mediator keeps all the information with
wrappers, so the estimated or provided information can be fetched from wrapper. Cost based optimization techniques
are further divided in different types. Few of them are discussed below:
 Disco (Distributed Information Search Component): Tomasic et al., [2], define the user of web as end users
(Focus on data), application developers (focus on presentation), Database Administrators (focus on wrapping) and
Database Implementers (focus on performance). Naacke [10], Disco is a cost based optimization technique. It combines
a generic cost model with specific cost information exported by wrappers The Disco approach deals with the unavailable
data. The Disco mediator contains its own internal database (see Figure 3) [2, 10 & 23]. Disco returns a biased answer.
In the internal database on Disco it contains interfaces, views, data information, registration details etc. By [23], there are
two basic algorithms are used named as cost formula integration and cost estimation. First algorithm is used for root to
leaf, formulas are applied on nodes and in these rules are used to connect with the mediator cost model and 2nd algorithm
Volume 2, Issue 12, December 2013
Page 103
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
is applied for its back traversal which is based on plans and operator’s cost is computed. In this approach, from the root to
the leaves and back method is used to calculate the cost.
Figure 3: Disco Architecture
 Garlic Architectures: Garlic [18, 20 and 21] approach is used for heterogeneous multimedia (text, videos,
images etc.) information. An incorporated view of heterogeneous information sources shown in Figure 4 is presented by
this approach.
Figure 4: Garlic Architecture
The query processor in the garlic architecture generates plans to apply accurate query on the heterogeneous repositories of
information. This approach decomposes the quires and repositories in to pieces to distribute the load of repositories. In
the Garlic approach wrappers plays an important role and with the help of wrappers this technique follows and improve
the cost estimation techniques. In this framework, cost model, cost formulas, and statistics techniques are used for cost
estimation.
 Ariadne: Ambite and Knoblock [15] proposed a new approach PbR (Planning by Rewriting) based on mediator
approach. The planning for a query is most important. For example it is worth to plan more and more if the total cost is
very small. With the flexibility of PbR approach the planning is applied on different scale projects differently and in its
continuation it reduces the cost. Secondly, in PbR an initial plan is followed by rewriting rules. The rewriting rules are
iteratively applied until a satisfactory result or a resource limit is reached.
 Hermes (Heterogeneous Reasoning and Mediator System): Subrahmanian et al.,[33] proposed a mediator
language for consistency to access data sources and reasoning system. Further they discuss main aspects of mediator
approach. Domain integration is used to add new data or reasoning system to maintain the whole system. Whereas, in
Semantic Integration the procedure used to determine conflicts, to find mutual pool information, and define new
operations. In Hermes, query optimization is based on cost-based model, because it uses caching technique [28]. All the
calls which are locally available are not sent to the data servers. With this technique, the execution time is improved
and further the cost of possible execution plans is estimated.
3.2 Quality-Based Optimization Techniques
In Web-centric query optimization the quality of the information is more important. In these cases, the cost is estimated
with different parameters. These optimization techniques are as much important as others and few of them are as
follows:
Volume 2, Issue 12, December 2013
Page 104
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
 ObjectGlobe: Braumandl et al., [26] developed a distributed query processing system based on quality of service
(QoS) and query evaluation plan (QeP). The authors [26] proposed new services named as cycle and function providers
for the users and the information providers. The developers Braumandl et al., [26], compare their system with the garlic
systems. In the garlic system no new functionality can be transport to the data, but this technique is more dynamic and
it is possible. The researchers [26 & 23] propose the four major steps i) lookup service is queried to find relevant
information sources, cycle providers, and query operators that might be useful to execute the query. Some more
information regarding cost is also collected during lookup ii) an optimal plan is built to fulfil the quality constraints of
the user, based on information gathered from lookup iii) Plug in this step the plan is spread to the external query
operators and the cycle providers iv) in the last step the plan is executed with an iterator model. Further, the quality
constraints are applied on each and every phase. The query plan is dynamically adapted or the query is aborted, based
on that QoS concept, the goal of ObjectGlobe is to increase the ratio of successful queries.
 HiQIQ (High Quality Information Querying) Naumann and Lesser [5] incorporated Information Quality (IQ)
in the query plan. The approach is a combination of source centric and query-centric approaches used in mediators.
Different criteria are applied in Information Quality (IQ) with query planning. The first criteria, which are Query
Correspondence Assertions (QCA) specific criteria, include response time, availability, price, accuracy, relevancy and
consistency. The second, query-specific criteria check the quality of the answer received corresponding to each query.
The Information Quality is applied based on above criteria to improve the quality.
3.3 Adaptive query optimization
Adaptive or dynamic query optimization is most important in heterogeneous web-centric query optimization technique.
These techniques remove the unpredictable events during query execution. Adaptive Query Optimization techniques are
active at run time to change the execution plan, repeat the optimization query with some specific operators to increase
the flexibility. The adaptive query, dynamic optimization finds its applications in various projects and approaches.
 Telegraphy: Hellerstein [16] et al., proposed dataflow architecture based on rivers and eddies named as
telegraphy query engine. Telegraphy is architected for fine-grained adaptivity and interactive user control. To improve
both the extent and the effects is the main goal of telegraphy. In this approach a metadata is used to store the
information at end of each query and this information is used for the next queries, whereas to enhance the set of effects
from rivers and eddies is more complicated. The working of eddy [27] is as in the following steps i) the eddy set order of
tuples to operators, ii) the operators executes an independent thread, iii)and back it return tuples to the eddy. The eddy
sends a tuple to the output only when it has been handled by all the operators.
 Interleaving scheduling and optimization: Bouganim [17] proposed a new dynamic scheduling approach to
deal with limited memory. The researchers basically focus on the performance problems due to random and wanted data
release rates in data integration systems. This approach monitors the arrival rates at the information sources and
memory availability. Further, the execution plan is revised as there is any significant change. The above statements
prove that planning and execution phases are interleaved. In their query execution plan they proposed two types of
edges names as blocking edge and pipelineable edge respectively. In the first edge the data is completely produced
before consumed, it means the blocking input have to wait for the whole data before start. Whereas, on another edge the
consumer can start with a single tuple and no need to wait.
Further, Query engine divides the query execution into several components: dynamic query optimizer, dynamic query
scheduler and processor, and communication manager [17]. The dynamic query optimizer implements dynamic reoptimization techniques. Each planning phase of the dynamic query optimizer can modify the query execution plan
(QEP). Dynamic query scheduler takes the input from QEP and schedule the planning. The dynamic query processor
implements the execution component of the system and processes concurrently. The Communication Manager (CM)
receives data from the wrappers and makes it available to the dynamic query processor.
3.4 Optimizing queries over sources with limited capabilities
The web-centric queries optimization technique deals with different sources and queries. In the web queries the
sources follow the specific requirements and limited constraints. For example, users can send only one query at a time.
He/she can’t check the whole database with a single query. There are various reasons behind to send a single query at a
time. The main reasons are: i) security ii) performance and iii) specific business. To overcome on these problems there
are many query optimization techniques named as Tsimmis, Infomaster, Information Manifold, Annotated query plans
and source sequence plan with limited capabilities. In analysis, Tsimmis and Infomaster are important so these are
discussed in detail.
 Tsimmis: Molina et al., [12], proposed a prototype to integrate volatile information sources for mediation. The
basic principles of Tsimmis (Figure 5 and Figure 6) [12 & 23] are: i) Object Exchange Model (OEM) is a lightweight due
Volume 2, Issue 12, December 2013
Page 105
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
to less typing objects, it convey the information between components, ii) Mediator specification Language (MSL) is logic
based which targets OEM, iii) the extension of MSL are wrappers or translators which are specific with Wrapper
Specification Language (WSL) to permit for the description of source contents and querying capabilities iv) Wrapper- and
mediator-generators. Tsimmis approach focuses on feasible query plans with limited capabilities of available sources.
Figure 5: Tsimmis Prototype
Figure 6: Tsimmis Query Processor
 Infomaster: By Duschka & Genesereth [24], Infomaster gives an illusion of centralized volatile information
system. It an information integration system for homogeneous data. For mediation, Infomaster [Figure 7] is sourcecentric, with different type of relations: i) for users to prepare queries interface-relations are available ii) for data
information source-relations are available and iii) to generate a schema for the system and to describe the above relation a
world-relation is used.
Figure 7: Infomaster Architecture
By [23 and 24], the working of query processor in Infomaster follow the five basic steps i) Reduction: in this step the
system rewrite each atom of query in terms of base relations ii) Abduction: base relations based expression is produced
and with a set of definitions. The set of all consistent and minimal conjunctions and retrievable atoms is produced iii)
Conjunctive minimization: this step eliminates all the redundant conjunct iv) Disjunctive minimization: in this step
Infomaster drops all disjuncts that can be shown in the union of the remaining disjuncts v) Grouping: the atoms are
grouped within each conjunction for presentation.
Table 1 lists the comparison of the optimization techniques presented so for. It is evident that none of technique can be
called as the best and in fact all approaches are application specific rather than being generic.
Table 1 : Comparative Analyses of Various Classical Query Optimization
Volume 2, Issue 12, December 2013
Page 106
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
4. CONCLUSIONS
In addition to difficulties encountered in optimizing queries various query optimization techniques are explored. To
optimize the query and to control query execution is a difficult after submitted the query on the web. Further, with these
problems the information source may show different behaviour from its expectations. So, it may reduce the expected
results. It shows that, the traditional optimization techniques that are based on static information may not be appropriate
for heterogeneous information. Further, expectations from web are increasing day by day and as its result the dynamic
nature of web is also changing. Web centric query optimization could not work in the boundaries of classical query
optimization techniques.
REFERENCES:
[1] A. Tomasic, L. Rashid, and P. Valduriez, “Scaling heterogeneous database and design of DISCO,” in Proceedings of
the 16th International Conference on Distributing Computing Systems (ICDCS), Hong Kong, May 1996.
[2] Andrea Cali and Diego Calvanese, “Optimising Query Answering in the Presence of Access Limitations”, in
proceedings of the 17th International Conference on Database and Expert Systems Applications (DEXA'06), 0-76952641-1/06, IEEE Computer Society-2006.
[3] Atul Garg and Dimple Juneja “A Comparison and Analysis of various extended Techniques of Query Optimization”,
in International Journal of Advancements in Technology, Vol. 3(3), July 2012, ISSN : 0976-4860 (Online).
[4] Chaitali Gupta, Rajdeep Bhowmik, Michael R. Head, Madhusudhan Govindaraju, and Weiyi Meng, “Improving
Performance of Web Services Query Matchmaking with Automated Knowledge Acquisition” 2007 IEEE/WIC/ACM
International Conference on Web Intelligence, 0-7695-3026-5/07, IEEE Computer Society-2007.
[5] Felix Naumann, Ulf Lesser, “Quality–driven integration of heterogeneous information systems,” in Proceedings of
the 25th International Conference on Very Large Data Bases (VLDB), Edinburgh, UK, Sept. 1999.
[6] G. Wiederhold, “Mediators in the architecture of future information systems,” IEEE Computer, vol. 25, no. 3,1992.
[7] G. Unel, M.E. Donderler, O. Ulusoy and U. Gudukbay, “An efficient query optimization strategy for spatio-temporal
queriesin video databases”, The Journal of Systems and Software 73 (2004) 113–131, www.elsevier.com/locate/jss, 5
December 2003.
[8] G. Wiederhold, “Mediators in the architecture of future information systems”, IEEE Computer, vol. 25, no. 3, 1992.
[9] Gustavo Rossi, Daniel Schwabe and Robson Guimaraes, “Designing Personalized Web Applications”, WWW10,
Hong Kong, China, Copyright 2001 ACM 1-58113-000-0/00/0000, May 1-4, 2000.
[10] H. Naacke, G. Gardarin, and A. Tomasic, “Leveraging mediator cost models with heterogeneous data sources,” in
Proceedings of the 14th International Conference on Data Engineering, Orlando, Florida, Feb. 1998.
[11] http://en.wikipedia.org/wiki/Query_optimization
[12] H. Garcia-Molina, Y. Papakonstantinou, D. Quass, A. Rajaraman, Y. Sagiv, J.D. Ullman, V. Vassalos, and J.
Widom, “The TSIMMIS approach to mediation: Data models and languages,” Journal of Intelligent Information
Systems, vol. 8, no. 2, 1997.
[13] J. Meinecke, M. Gaedke, F. Majer, and A. Brändle, “Capturing the Essentials of Federated Systems”, in proceedings
of the Fourteenth International World Wide Web Conference (WWW), Pages 895-896, Edinburgh, UK, 23-26 May,
2006.
[14] Jianying Hu and Amit Bagga, “Categorizing Images in Web Documents”, 1070-986X/04, IEEE Computer Society,
January-March, 2004.
[15] Jous Luis Ambite and Craig A. Knoblock, “Flexible and scalable query planning in distributed and heterogeneous
environments,” in proceedings of the Fourth International Conference on Artificial Intelligence Planning Systems,
Pitsburg, USA, June 1998.
[16] J.M. Hellerstein, M.J. Franklin, S. Chnadrasekaran, A. Deshpande, K. Hildrum, S. Madden, V. Ramana, and M.A.
Shah, “Adaptive query processing: Technology in evolution,” IEEE Data Engineering Bulletin, vol. 23, no. 2, 2000.
[17] L. Bouganim, F. Fabret, C. Mohan, and P. Valduriez, “Dynamic query scheduling in data integration systems,”in
Proceedings of the 16th International Conference on Data Engineering, San Diego, CA, USA, Feb.–March 2000.
[18] M. Carey, L. Haas, et al., “Towards Heterogeneous Multimedia Information Systems: The Garlic Approach”, RIDEDOM ‘95, Taiwan 3/95. (5th International Workshop on Research Issues in Data Engineering: Distributed Object
Management.)
[19] M. E. Donderler, O. Ulusoy and U. Gudukbay, “A rule-based approach to represent spatio-temporal relations in video
data”, In: Yakhno, T. (Ed.), Advances in Information Systems (ADVIS’00), Lecture Notes in Computer Science,
1909, Springer, Berlin, pp. 409–418, 2000.
[20] M.T. Roth and P. Schwarz, “Don’t scrap it, wrap it! A wrapper architecture for legacy data sources”, in Proceeedings
of the 23rd International Conference on Very Large Data Bases (VLDB), Athens, Greece, Aug. 1997.
[21] M.T. Roth, M Arya, L Haas, M Carey, W Cody, R Fagin, P Schwarz, J Thomas, E Wimmers and P. Schwarz, “The
Garlic Project”, pp 557, ACM, 1997.
[22] Mengchi Liu and Tok Wang Ling, “A Rule-based Query Language for HTML”, 0-7695-0996-7/01, IEEE-2001.
Volume 2, Issue 12, December 2013
Page 107
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 12, December 2013
ISSN 2319 - 4847
[23] Mourad Ouzzani and Athman Bouguettaya, “Query Processing and Optimization on the Web”, Distributed and
Parallel Databases, Kluwer Academic Publishers. Manufactured in The Netherlands, 15, 187–218, 2004.
[24] O.M. Duschka and M.R. Genesereth, “Query planning in infomaster,” in Proceedings of the Twelfth Annual ACM
Symposium on Applied Computing, SAC ’97, San Jose, CA, USA, Feb. 1997.
[25] R. H. Arpaci-Dusseau, E. Anderson, N. Treuhaft, D.E. Culler, J.M. Hellerstein, D. Patterson, and K. Yelick, “Cluster
I/O with River: Making the fast case common,” in proceedings of the Sixth Workshop on I/O in Parallel and
Distributed Systems. ACM Press, May 1999.
[26] Reinhard Braumandl, Markus Keidl, Alfons Kemper, Donald Kossmann, Stefan Seltzsam, Konrad Stocker,
“ObjectGlobe: Open Distributed Query Processing Services on the Internet,” in the Bulletin of the Technical
Committee on March 2001 Vol. 24 No. 1, IEEE Computer Society.
[27] Ron Avnur and Joseph M. Hellerstein, “Eddies: Continuously adaptive query processing,” in Proceedings of the
ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, May 2000.
[28] S. Adali, K.S. Candan, Y. Papakonstantinou, and V.S. Subrahmanian, “Query caching and optimization in
distributed mediator systems,” in Proceeedings of ACM SIGMOD International Conference on Management of Data,
Montreal, Canada, June 1996.
[29] S. V. U. M. Rao., “Parametric query optimization: A non-geometric approach”, Technical report, Indian Institute of
Technology, Kanpur, Available at http://www.cse.iitk.ac.in/research/mtech1997, Feb 1999.
[30] Sunny K.S. Lam and M. Tamer Ozsu, “Querying Web Data – The WebQA Approach”, in the proceedings of the 3rd
International Conference on Web Information Systems Engineering (WISE.02), 0-7695-1766-8/02, IEEE Computer
Society, 2007.
[31] Utkarsh Srivastava, Kamesh Munagala, Jennifer Widom and Rajeev Motwani, “Query Optimization over Web
Services”, VLDB ‘06, Seoul, Korea, ACM 1595933859/06/09, 2006.
[32] Valeria Cardellini, Emiliano Casalicchio and Michele Colajanni, “A Performance Study of Distributed Architectures
for the Quality of Web Services”, in proceedings of the 34th Hawaii International Conference on System Sciences –
2001, 0-7695-0981-9/01, IEEE, 2001.
[33] V.S. Subrahmanian, Sibel Adali, Anne Brink, James J. Lu, Adil Rajput, Timothy J. Rogers, Robert Ross, Charles
Ward, “A Heterogeneous Reasoning and Mediator System”, http://www.cs.umd.edu/projects/hermes/overview/paper/.
Atul Garg received degree of Master of Computer Applications from Kurukshetra University, Kurukshetra in
2004 and completed his Ph. D degree from Maharishi Markandeshwar University, Mullana (Ambala) in 2013.
Currently, he is working as an Associate Professor at M. M. I. C. T. & B. M., Maharishi Markandeshwar
University, Mullana (Ambala), Haryana. His area of interest is web and Query Optimizations.
Volume 2, Issue 12, December 2013
Page 108