In this paper we introduce a distributed information retrieval

system using agent-based technology. In this multiagent

system, each agent has its own specific task and can

be used to handle a specific document repository. The system

is designed to automatically comply with access restriction

rules that are normally enforced in companies. It is

used in the administration offices of the German capital city

Berlin where it serves as a testbed for further research on

aggregated search in an enterprise environment with roughly

50,000 employees

Albayrak, Sahin

Gunadi, Erwin

Hopfgartner, Frank

Meder, Michael

Plumbaum, Till

Scheel, Christian

English

Enlighten

Distributed Enterprise Search using Software AgentsErwin Gunadi, Michael Meder, Till Plumbaum, Frank Hopfgartner, and Sahin AlbayrakTechnische Universität BerlinTEL 14, Ernst-Reuter-Platz 710587 Berlin, Germany{firstname.lastname}@dai-labor.deABSTRACTIn this paper we introduce a distributed information re-trieval system using agent-based technology. In this multi-agent system, each agent has its own specific task and canbe used to handle a specific document repository. The sys-tem is designed to automatically comply with access restric-tion rules that are normally enforced in companies. It isused in the administration offices of the German capital cityBerlin where it serves as a testbed for further research onaggregated search in an enterprise environment with roughly50,000 employees.Keywordsdistributed information retrieval, enterprise, software agents1. INTRODUCTIONEnterprise environments face scattered and unstructuredinformation, as data is created in different formats across dif-ferent places [7]. This makes searching in such environmentchallenging, since established search algorithms and tech-niques cannot easily be transferred to an enterprise scenario[7, 8, 10]. Hence, research on the development of appropri-ate desktop and enterprise systems is required. Hawking [2,3] and Mukherjee et al. [7] identify various challenges thatarise from such environments: First of all, heterogeneousdocument types are created, which are stored in multipledocument repositories. Moreover, different access restric-tions need to be considered, i.e., not everyone is allowed toaccess all data in an enterprise. While some data is avail-able for every employee (e.g., a company directory serviceor the company’s web pages), more sensitive data (e.g., em-ployees’ personal data, blueprints of top secret prototypes,etc.) might be accessible by a minority only. Moreover,the sensitive nature of data available in an enterprise raisesspecific demands on data security [2]. In particular, usingcentralized indices can be seen as a major security risk forenterprise search systems.In this demo paper, we introduce an enterprise search sys-tem with distributed indices which addresses the data accu-mulation task of enterprise search systems. The frameworkincorporates the idea of data mining agents, a techniquewhich has been successfully employed to create data ware-houses [6]. We use autonomous agents for every task inthe data accumulation and indexing activity, i.e., each agentprovides core services that cover a specific part in the back-end. Complex tasks such as crawling and indexing a fileserver is achieved by combining the corresponding agents,i.e., the autonomous agents form a community to providea joint service in creating search engine capabilities. Whenmultiple data repositories (collections) need to be indexedwe use these agent communities to build a distributed searchengine. Search request are handled by broker agents thatverify users’ identity and their access rights using the en-terprise’s directory access constraints that are defined usingLightweight Directory Access Protocol (LDAP).The paper is structured as follows. In Section 2, we re-view state-of-the-art research related to enterprise search.Section 3 provides an overview of our system. Section 4concludes this paper.2. RELATEDWORKExisting enterprise search systems can be classified intotwo categories: (1) systems that create one centralized indexand (2) systems that create many distributed independentindices. A centralized index can be used when it is possi-ble to crawl all of the relevant data sources into a singleindex structure. However, since in most cases information isstored at different locations and due to physical constraintssuch as geographical location, low bandwidth connectionsand administration restrictions, gathering data in one searchindex is not always feasible [3]. Another important featureof enterprise search system is that it has be able to handlesecurity and rights management issues [7, 2].We address the issue of building distributed independentindices by applying software agents. Jennings et al. [5] pro-vide a detailed comparison of the agent and other softwareengineering paradigms. One of the advantages of this con-cept is that we can model each agent to handle differentunique tasks. A similar approach has been introduced byZhou et al. [11], who, however, relies on ontologies to modeluser access. Our system is more flexible since its user accessis managed automatically by exploiting the existing accessrights saved in LDAP.Note that currently, there are already some commercialenterprise search products available that provide distributedsearch to some extent. Following recent revelations in thenews though, it remains unclear whether such proprietaryoff-the-shelf systems really comply with strict informationprivacy rules, especially when outside of US legislation. There-fore, government agencies e.g., in Germany are advised torely on alternative systems that allow them to maintain con-trol over their data. Addressing this issue, our system iscurrently trialed in the administration offices of a large Eu-ropean capital city, allowing us to study open research chal-lenges in the field using real users in real context.3. SYSTEM OVERVIEWIn order to accumulate data from distributed sources, werely on autonomous software agents that can be combinedto build joint services. We propose to provide autonomousagents for every task in the data accumulation and indexingactivity, i.e., each agent provides a core service that coversa specific part in the back-end. Example agents includecrawling agents based on Nutch or search agents based onSolr. The combination of these agents forms a distributedsearch architecture that individually crawls and indexes allof the available repositories and create multiple independentsearch verticals. Search requests are received by a brokeragent via REST-APIs and the broker agent will delegatethese requests to the various search agents. An example ofthe combination of these agents is shown in Figure 1.Figure 1: Broker Agent delegates search requests todifferent search agents on indices prepared by crawl-ing agentsDue to the diverse nature of the collections, some collec-tions such as company external websites do not require anyauthentication while data repositories or wiki pages requireit. The broker agent guarantees that the company’s folderrestriction rights are respected by communicating with thecompany’s LDAP server.For organization and implementation of these agents andtheir communication, we use the JIAC (Java-based Intel-ligent Agent Componentware) framework [4]. JIAC is asoftware-agent framework that has been optimized for large-scale applications and services. Agents are arranged on plat-forms, allowing the arrangement of agents that belong to-gether with the control of at least one manager. Further,JIAC supports encrypted communication and user autho-rization.4. CONCLUSION AND FUTUREWORKIn this demo, we introduce a document crawling and in-dexing system which can be applied in a desktop and enter-prise search scenario. As we have outlined, the typical ITinfrastructure of enterprise intranets requires flexible dataaggregation methods that are able to handle distributedsources. We propose the use of autonomous software agentswhich communicate via a common agent framework. Advan-tages of this approach are the agent’s flexibility in handlingdistributed infrastructures as well as the easy possibilities toexpand the framework’s services further, i.e., by combiningexisting agents or by adding new software agents.The introduced approach is currently used as the data ag-gregation method of a Desktop and Enterprise search systemof the administration of Berlin with roughly 50,000 employ-ees, thus assisting a large user base in their professional in-formation gathering task. As future work, we aim to studynovel approaches for federated search [1, 9] and analyze in-teraction logs of these users to further study open researchquestions, e.g., on content personalization and aggregatedresult ranking. A demo of the system can be accessed onhttp://pia-demo.dai-labor.de/AcknowledgementWe acknowledge the support of Christian Scheel, StevenWinter, Jens Meiners, and Arif Oksal. The system has bendeveloped under the umbrella of IDBB, a research collabo-ration between DAI-Labor and ITDZ Berlin.5. REFERENCES[1] J. Callan. Distributed information retrieval. InAdvances in Information Retrieval, pages 127–150.Kluwer Academic Publishers, 2000.[2] D. Hawking. Challenges in enterprise search.Proceedings of the 15th Australasian databaseconference - Volume 27, 2004.[3] D. Hawking. Enterprise Search. In R. Baeza-Yates andB. Ribeiro-Neto, editors, Modern InformationRetrieval, pages 641–684. Addison-Wesley, 2ndedition, 2010.[4] B. Hirsch, T. Konnerth, and A. Heßler. MergingAgents and Services — the JIAC Agent Platform. InMulti-Agent Programming: Languages, Tools andApplications, pages 159–185. Springer, 2009.[5] N. R. Jennings and M. Wooldridge. Agent-orientedsoftware engineering. Artificial Intelligence,117:277–296, 2000.[6] M. Klusch, S. Lodi, and G. Moro. Agent-BasedDistributed Data Mining: The KDEC Scheme. InAgentLink, pages 104–122, 2003.[7] R. Mukherjee and J. Mao. Enterprise search: Toughstuff. Queue, 2(2):36–46, 4 2004.[8] J. Peng, C. Macdonald, B. He, and I. Ounis. A studyof selective collection enrichment for enterprise search.In CIKM, pages 1999–2002, 2009.[9] M. Shokouhi and L. Si. Federated Search. Foundationsand Trends in Information Retrieval, 5(1):1–102, 2011.[10] W. Zheng, H. Fang, C. Yao, and M. Wang. Searchresult diversification for enterprise data. Proceedings ofthe 20th ACM international conference on Informationand knowledge management, pages 1901–1904, 2011.[11] L. Zhou. Multi-agent based distributed secureinformation retrieval. In CMC’10, volume 1, pages76–79, 2010.

Distributed enterprise search using software agents

Published version freely available via IFAAMAS repository (http://www.ifaamas.org/Proceedings/aamas2014/) and ACM Digital Library (https://dl.acm.org/citation.cfm?id=2616094)In this paper we introduce a distributed information retrieval system using agent-based technology. In this multi-agent system, each agent has its own specific task and can be used to handle a specific document repository. The system is designed to automatically comply with access restriction rules that are normally enforced in companies. It is used in the administration offices of the German capital city Berlin where it serves as a testbed for further research on aggregated search in an enterprise environment with roughly 50,000 employees

DepositOnce

Distributed Enterprise Search using Software Agents(Demonstration)Erwin Gunadi, Michael Meder, Till Plumbaum, Christian Scheel, Frank Hopfgartnerand Sahin AlbayrakTechnische Universität Berlin10587 Berlin, Germany{firstname.lastname}@dai-labor.deABSTRACTIn this paper we introduce a distributed information re-trieval system using agent-based technology. In this multi-agent system, each agent has its own specific task and canbe used to handle a specific document repository. The sys-tem is designed to automatically comply with access restric-tion rules that are normally enforced in companies. It isused in the administration offices of the German capital cityBerlin where it serves as a testbed for further research onaggregated search in an enterprise environment with roughly50,000 employees.Categories and Subject DescriptorsH.3.4 [Systems and Software]: Distributed SystemsKeywordsdistributed information retrieval; enterprise; software agents1. INTRODUCTIONEnterprise environments face scattered and unstructuredinformation, as data is created in different formats across dif-ferent places [7]. This makes searching in such environmentchallenging, since established search algorithms and tech-niques cannot easily be transferred to an enterprise scenario[7, 8, 10]. Hence, research on the development of appropri-ate desktop and enterprise systems is required. Hawking [2,3] and Mukherjee et al. [7] identify various challenges thatarise from such environments: First of all, heterogeneousdocument types are created, which are stored in multipledocument repositories. Moreover, different access restric-tions need to be considered, i.e., not everyone is allowed toaccess all data in an enterprise. While some data is avail-able for every employee (e.g., company’s web pages), moresensitive data (e.g., blueprints of top secret prototypes, etc.)might be accessible by a minority only. Moreover, the sensi-tive nature of data available in an enterprise raises specificdemands on data security [2]. In particular, using central-ized indices can be seen as a major security risk for enterprisesearch systems.Appears in: Alessio Lomuscio, Paul Scerri, Ana Bazzan,and Michael Huhns (eds.), Proceedings of the 13th Inter-national Conference on Autonomous Agents and MultiagentSystems (AAMAS 2014), May 5-9, 2014, Paris, France.Copyright c© 2014, International Foundation for Autonomous Agents andMultiagent Systems (www.ifaamas.org). All rights reserved.In this demo paper, we introduce an enterprise search sys-tem with distributed indices which addresses the data accu-mulation task of enterprise search systems. The frameworkincorporates the idea of data mining agents, a techniquewhich has been successfully employed to create data ware-houses [6]. We use autonomous agents for every task inthe data accumulation and indexing activity, i.e., each agentprovides core services that cover a specific part in the back-end. Complex tasks such as crawling and indexing a fileserver is achieved by combining the corresponding agents,i.e., the autonomous agents form a community to providea joint service in creating search engine capabilities. Whenmultiple data repositories (collections) need to be indexedwe use these agent communities to build a distributed searchengine. Search request are handled by broker agents thatverify users’ identity and their access rights using the en-terprise’s directory access constraints that are defined usingLightweight Directory Access Protocol (LDAP).The paper is structured as follows. In Section 2, we re-view state-of-the-art research related to enterprise search.Section 3 provides an overview of our system. Section 4concludes this paper.2. RELATEDWORKExisting enterprise search systems can be classified intotwo categories: (1) systems that create one centralized indexand (2) systems that create many distributed independentindices. A centralized index can be used when it is possi-ble to crawl all of the relevant data sources into a singleindex structure. However, since in most cases information isstored at different locations and due to physical constraintssuch as geographical location, low bandwidth connectionsand administration restrictions, gathering data in one searchindex is not always feasible [3]. Another important featureof enterprise search system is that it has to be able to handlesecurity and rights management issues [7, 2].We address the issue of building distributed independentindices by applying software agents. Jennings et al. [5] pro-vide a detailed comparison of the agent and other softwareengineering paradigms. One of the advantages of this con-cept is that we can model each agent to handle differentunique tasks. A similar approach has been introduced byZhou et al. [11], who, however, relies on ontologies to modeluser access. Our system is more flexible since its user accessis managed automatically by exploiting the existing accessrights saved in LDAP.Note that currently, there are already some commercialenterprise search products available that provide distributed1623search to some extent. It remains unclear though whethersuch proprietary off-the-shelf systems really comply withstrict information privacy rules. Therefore, government agen-cies e.g., in Germany are advised to rely on alternative sys-tems that allow them to maintain control over their data.Addressing this issue, our system is currently trialed in theadministration offices of the city of Berlin, allowing us tostudy open research challenges in the field using real usersin real context.3. SYSTEM OVERVIEWIn order to accumulate data from distributed sources, werely on autonomous software agents that can be combinedto build joint services. We propose to provide autonomousagents for every task in the data accumulation and indexingactivity, i.e., each agent provides a core service that cov-ers a specific part in the back-end. Example agents includecrawling agents based on Nutch1 or search agents based onSolr2. The combination of these agents forms a distributedsearch architecture that individually crawls and indexes allof the available repositories and create multiple independentsearch verticals. Search requests are received by a brokeragent via REST-APIs. When user authorization is requiredthe broker agent communicates with the LDAP server toverify user account and will fetch user’s access permissionsafter successful login. It is used to query available searchagents. Each search agent then limits the found documentsaccording to the user’s permission. An example of the com-bination of these agents is shown in Figure 1.Figure 1: Broker Agent delegates search requests todifferent search agentsFor organization and implementation of these agents andtheir communication, we use the JIAC (Java-based Intel-ligent Agent Componentware) framework [4]. JIAC is asoftware-agent framework that has been optimized for large-scale applications and services. It gives us a tool to createspecialized agents and to build a multi-agent system. Fur-ther, JIAC supports encrypted communication and user au-thorization.1http://nutch.apache.org/2https://lucene.apache.org/solr/4. CONCLUSION AND FUTUREWORKIn this demo, we introduce a document crawling and in-dexing system which can be applied in a desktop and enter-prise search scenario. As we have outlined, the typical ITinfrastructure of enterprise intranets requires flexible dataaggregation methods that are able to handle distributedsources. We propose the use of autonomous software agentswhich communicate via a common agent framework. Advan-tages of this approach are the agent’s flexibility in handlingdistributed infrastructures as well as the easy possibility toexpand the framework’s services further, i.e., by combiningexisting agents or by adding new software agents.The introduced approach is currently used as the data ag-gregation method of a Desktop and Enterprise search systemof the administration of Berlin with roughly 50,000 employ-ees, thus assisting a large user base in their professional in-formation gathering task. As future work, we aim to studynovel approaches for federated search [1, 9] and analyze in-teraction logs of these users to further study open researchquestions, e.g., on content personalization and aggregatedresult ranking. A demo of the system can be accessed onhttp://pia-demo.dai-labor.de/AcknowledgementWe acknowledge the support of Steven Winter, Jens Mein-ers, Marcus Adler and Arif Oksal. The system has beendeveloped under the umbrella of IDBB, a research collabo-ration between DAI-Labor and ITDZ Berlin.5. REFERENCES[1] J. Callan. Distributed information retrieval. InAdvances in Information Retrieval, pages 127–150.Kluwer Academic Publishers, 2000.[2] D. Hawking. Challenges in enterprise search.Proceedings of the 15th Australasian databaseconference - Volume 27, 2004.[3] D. Hawking. Enterprise Search. In R. Baeza-Yates andB. Ribeiro-Neto, editors, Modern InformationRetrieval, pages 641–684. Addison-Wesley, 2010.[4] B. Hirsch, T. Konnerth, and A. Heßler. MergingAgents and Services - the JIAC Agent Platform. InMulti-Agent Programming: Languages, Tools andApplications, pages 159–185. Springer, 2009.[5] N. R. Jennings and M. Wooldridge. Agent-orientedsoftware engineering. Artificial Intelligence,117:277–296, 2000.[6] M. Klusch, S. Lodi, and G. Moro. Agent-BasedDistributed Data Mining: The KDEC Scheme. InAgentLink, pages 104–122, 2003.[7] R. Mukherjee and J. Mao. Enterprise search: Toughstuff. Queue, 2(2):36–46, 4 2004.[8] J. Peng, C. Macdonald, B. He, and I. Ounis. A studyof selective collection enrichment for enterprise search.In CIKM, pages 1999–2002, 2009.[9] M. Shokouhi and L. Si. Federated Search. Foundationsand Trends in Information Retrieval, 5(1):1–102, 2011.[10] W. Zheng, H. Fang, C. Yao, and M. Wang. Searchresult diversification for enterprise data. CIKM, pages1901–1904, 2011.[11] L. Zhou. Multi-agent based distributed secureinformation retrieval. In CMC’10, pages 76–79, 2010.1624

Enlighten: Publications

Distributed Enterprise Search using Software Agents
Erwin Gunadi, Michael Meder, Till Plumbaum, Frank Hopfgartner, and Sahin Albayrak
Technische Universität Berlin
TEL 14, Ernst-Reuter-Platz 7
10587 Berlin, Germany
{firstname.lastname}@dai-labor.de
ABSTRACT
In this paper we introduce a distributed information re-
trieval system using agent-based technology. In this multi-
agent system, each agent has its own specific task and can
be used to handle a specific document repository. The sys-
tem is designed to automatically comply with access restric-
tion rules that are normally enforced in companies. It is
used in the administration offices of the German capital city
Berlin where it serves as a testbed for further research on
aggregated search in an enterprise environment with roughly
50,000 employees.
Keywords
distributed information retrieval, enterprise, software agents
1. INTRODUCTION
Enterprise environments face scattered and unstructured
information, as data is created in different formats across dif-
ferent places [7]. This makes searching in such environment
challenging, since established search algorithms and tech-
niques cannot easily be transferred to an enterprise scenario
[7, 8, 10]. Hence, research on the development of appropri-
ate desktop and enterprise systems is required. Hawking [2,
3] and Mukherjee et al. [7] identify various challenges that
arise from such environments: First of all, heterogeneous
document types are created, which are stored in multiple
document repositories. Moreover, different access restric-
tions need to be considered, i.e., not everyone is allowed to
access all data in an enterprise. While some data is avail-
able for every employee (e.g., a company directory service
or the company’s web pages), more sensitive data (e.g., em-
ployees’ personal data, blueprints of top secret prototypes,
etc.) might be accessible by a minority only. Moreover,
the sensitive nature of data available in an enterprise raises
specific demands on data security [2]. In particular, using
centralized indices can be seen as a major security risk for
enterprise search systems.
In this demo paper, we introduce an enterprise search sys-
tem with distributed indices which addresses the data accu-
mulation task of enterprise search systems. The framework
incorporates the idea of data mining agents, a technique
which has been successfully employed to create data ware-
houses [6]. We use autonomous agents for every task in
the data accumulation and indexing activity, i.e., each agent
provides core services that cover a specific part in the back-
end. Complex tasks such as crawling and indexing a file
server is achieved by combining the corresponding agents,
i.e., the autonomous agents form a community to provide
a joint service in creating search engine capabilities. When
multiple data repositories (collections) need to be indexed
we use these agent communities to build a distributed search
engine. Search request are handled by broker agents that
verify users’ identity and their access rights using the en-
terprise’s directory access constraints that are defined using
Lightweight Directory Access Protocol (LDAP).
The paper is structured as follows. In Section 2, we re-
view state-of-the-art research related to enterprise search.
Section 3 provides an overview of our system. Section 4
concludes this paper.
2. RELATEDWORK
Existing enterprise search systems can be classified into
two categories: (1) systems that create one centralized index
and (2) systems that create many distributed independent
indices. A centralized index can be used when it is possi-
ble to crawl all of the relevant data sources into a single
index structure. However, since in most cases information is
stored at different locations and due to physical constraints
such as geographical location, low bandwidth connections
and administration restrictions, gathering data in one search
index is not always feasible [3]. Another important feature
of enterprise search system is that it has be able to handle
security and rights management issues [7, 2].
We address the issue of building distributed independent
indices by applying software agents. Jennings et al. [5] pro-
vide a detailed comparison of the agent and other software
engineering paradigms. One of the advantages of this con-
cept is that we can model each agent to handle different
unique tasks. A similar approach has been introduced by
Zhou et al. [11], who, however, relies on ontologies to model
user access. Our system is more flexible since its user access
is managed automatically by exploiting the existing access
rights saved in LDAP.
Note that currently, there are already some commercial
enterprise search products available that provide distributed
search to some extent. Following recent revelations in the
news though, it remains unclear whether such proprietary
off-the-shelf systems really comply with strict information
privacy rules, especially when outside of US legislation. There-
fore, government agencies e.g., in Germany are advised to
rely on alternative systems that allow them to maintain con-
trol over their data. Addressing this issue, our system is
currently trialed in the administration offices of a large Eu-
ropean capital city, allowing us to study open research chal-
lenges in the field using real users in real context.
3. SYSTEM OVERVIEW
In order to accumulate data from distributed sources, we
rely on autonomous software agents that can be combined
to build joint services. We propose to provide autonomous
agents for every task in the data accumulation and indexing
activity, i.e., each agent provides a core service that covers
a specific part in the back-end. Example agents include
crawling agents based on Nutch or search agents based on
Solr. The combination of these agents forms a distributed
search architecture that individually crawls and indexes all
of the available repositories and create multiple independent
search verticals. Search requests are received by a broker
agent via REST-APIs and the broker agent will delegate
these requests to the various search agents. An example of
the combination of these agents is shown in Figure 1.
Figure 1: Broker Agent delegates search requests to
different search agents on indices prepared by crawl-
ing agents
Due to the diverse nature of the collections, some collec-
tions such as company external websites do not require any
authentication while data repositories or wiki pages require
it. The broker agent guarantees that the company’s folder
restriction rights are respected by communicating with the
company’s LDAP server.
For organization and implementation of these agents and
their communication, we use the JIAC (Java-based Intel-
ligent Agent Componentware) framework [4]. JIAC is a
software-agent framework that has been optimized for large-
scale applications and services. Agents are arranged on plat-
forms, allowing the arrangement of agents that belong to-
gether with the control of at least one manager. Further,
JIAC supports encrypted communication and user autho-
rization.
4. CONCLUSION AND FUTUREWORK
In this demo, we introduce a document crawling and in-
dexing system which can be applied in a desktop and enter-
prise search scenario. As we have outlined, the typical IT
infrastructure of enterprise intranets requires flexible data
aggregation methods that are able to handle distributed
sources. We propose the use of autonomous software agents
which communicate via a common agent framework. Advan-
tages of this approach are the agent’s flexibility in handling
distributed infrastructures as well as the easy possibilities to
expand the framework’s services further, i.e., by combining
existing agents or by adding new software agents.
The introduced approach is currently used as the data ag-
gregation method of a Desktop and Enterprise search system
of the administration of Berlin with roughly 50,000 employ-
ees, thus assisting a large user base in their professional in-
formation gathering task. As future work, we aim to study
novel approaches for federated search [1, 9] and analyze in-
teraction logs of these users to further study open research
questions, e.g., on content personalization and aggregated
result ranking. A demo of the system can be accessed on
http://pia-demo.dai-labor.de/
Acknowledgement
We acknowledge the support of Christian Scheel, Steven
Winter, Jens Meiners, and Arif Oksal. The system has ben
developed under the umbrella of IDBB, a research collabo-
ration between DAI-Labor and ITDZ Berlin.
5. REFERENCES
[1] J. Callan. Distributed information retrieval. In
Advances in Information Retrieval, pages 127–150.
Kluwer Academic Publishers, 2000.
[2] D. Hawking. Challenges in enterprise search.
Proceedings of the 15th Australasian database
conference - Volume 27, 2004.
[3] D. Hawking. Enterprise Search. In R. Baeza-Yates and
B. Ribeiro-Neto, editors, Modern Information
Retrieval, pages 641–684. Addison-Wesley, 2nd
edition, 2010.
[4] B. Hirsch, T. Konnerth, and A. Heßler. Merging
Agents and Services — the JIAC Agent Platform. In
Multi-Agent Programming: Languages, Tools and
Applications, pages 159–185. Springer, 2009.
[5] N. R. Jennings and M. Wooldridge. Agent-oriented
software engineering. Artificial Intelligence,
117:277–296, 2000.
[6] M. Klusch, S. Lodi, and G. Moro. Agent-Based
Distributed Data Mining: The KDEC Scheme. In
AgentLink, pages 104–122, 2003.
[7] R. Mukherjee and J. Mao. Enterprise search: Tough
stuff. Queue, 2(2):36–46, 4 2004.
[8] J. Peng, C. Macdonald, B. He, and I. Ounis. A study
of selective collection enrichment for enterprise search.
In CIKM, pages 1999–2002, 2009.
[9] M. Shokouhi and L. Si. Federated Search. Foundations
and Trends in Information Retrieval, 5(1):1–102, 2011.
[10] W. Zheng, H. Fang, C. Yao, and M. Wang. Search
result diversification for enterprise data. Proceedings of
the 20th ACM international conference on Information
and knowledge management, pages 1901–1904, 2011.
[11] L. Zhou. Multi-agent based distributed secure
information retrieval. In CMC’10, volume 1, pages
76–79, 2010.


http://eprints.gla.ac.uk/101290/1/101290.pdf

Distributed enterprise search using software agents

Abstract

Similar works

Full text

Available Versions

Enlighten

DepositOnce

Enlighten: Publications