1

Business-intelligence Mining of Large Decentralized Multimedia Datasets

With a Distributed Multi-agent System

By:- Karima Qayumi

Third Year PhD Student

Supervisors:- Alex Norta and Tobias Ley

Tallinn University, Narva Mnt 29, 10120 Tallinn

Mail: - karima@tlu.ee

2nd March 2016

2

Few words about research publications

• The first publication was a symposium paper on IEEE international conference on cloud computing (IC2E, 2015), march 9-12, tempe, AZ, USA, link is : http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7092969

• The second publication is a conference paper that is accepted on 18th International Conference on Autonomous Agents and Multi-agent Systems ( ICAAMS 2016), in Venice, Italy, on 13-14 June 2016, link is : https://www.waset.org/conference/2016/06/venice/ICAAMS

3

Outline

• Introduction

• Background and Challenges

• Research Methodology

• Domain Analysis and Detail Design

• Architecture Overview and Behavior Definition

• Agent-based Communication Protocol and Model

• Conclusion and Future Work

4

Introduction

• Research Objective

• Definition of agent

• Multi-agent Systems (MAS)

• Distributed Data Mining (DDM)

• Business Intelligence Management (BIM)

• Agent-oriented Modelling (AOM)

5

Research Objective

Developing of a distributed Business Intelligence (BI) system that comprises different types of agents with the ability to discover knowledge from various resources (data sites of system locate in different physical location).

6

What is an agent in Artificial Intelligence?

According to [22] an agent could be:

A robot

An expert system

A software agent

7

Multi-agent Systems (MAS)

According to [5], the MAS is:

• Consist of autonomous agents that can interact and collaborate among each other

• Offer a new dimension for cooperation and coordination work in BI-system

• Support complex information systems development

• In heterogeneous environment, agents work collectively to solve specific problem.

8

Distributed Data Mining (DDM)

• Data mining focuses on the extraction of knowledge from centralized data sets [1].

• DDM with parallel processing, requires a special approach [2].

• Multi-agent System (MAS) is a promising approach for solving complicated data mining tasks in parallel processing [3, 4] .

9

Business Intelligence Management (BIM)

In [6] the definition of BIM is:

• Is a technology-driven process for analyzing data

• Encompasses a variety of tools, applications and methodologies

• Use to collect data from internal systems and external sources

10

Agent-oriented Modelling (AOM)

• The Art of Agent-Oriented Modeling presents a new conceptual model for developing software systems that are open, intelligent, and adaptive.

• It describes an approach for modeling complex systems that consist of people, devices, and software agents in a changing environment (sometimes known as distributed sociotechnical systems) [7].

11

Literature review

• Latest research demonstrates agent-based models with the DDM for improvement of DDM performance [10, 11, 12, 13, 14].

• An overview in [15] shows that “Since multi-agent systems are distributed and agents have proactive and reactive features which are very useful for Knowledge Management Systems, combining DDM with MAS for data intensive applications is appealing”.

• A DDM survey on 2014 [16] shows that “Several efforts have been devoted to enable DDM through MAS”.

12

Background and ChallengesSystem Type Existence issues Approach and

MethodologiesApply Multi-agent

system (MAS)Ref

 Distributed data mining (DDM) with MAS

 non-automatic, inadaptable, does not support parallel and distributed mining, interaction and integration of agents and data mining

•  Agent-Mining Disciplinary Framework

• The methodology is not mention

•  Agent-mining system• Agent-mining

knowledge management

• Agent- mining applications

In paper [10]

 Complex distributed system(online intelligent system ) with MAS

 Real challenge which faces analysts and designers during development of any multi-agent systems

 proposed methodology for the creation of an Online Intelligent System (OIS)

• members- manager agent,

• decision-assistant agent

• reporter-agent

 In paper [11]

 Multi Agent Based Business Intelligence System (MABBI)

 reduced latencies and decision automation

•  New architecture, called• MABBI• The methodology is not

mention

 The functions of agents are not clear in prototypes

 In paper [12]

 Parallel Data Mining Agent ( PADMA)

To reduce  parallel data-accessing operation, parallel hierarchical clustering, and web-based data visualization

•  an agent-based parallel DM system architecture

• The methodology is not mention

 Two intelligent datamining agents

 In paper [13]

13

Research Methodology

The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts that are broadly defined as constructs (vocabulary and symbols), models (abstractions and representations), methods (algorithms and practices), and instantiations (implemented and prototype systems) [9].

14Figure 1: Design-science research framework for the domain of information systems [9]

15

Research Methodology cont…There are 7 guidelines for a design science research[8]:

• Design as an artifact:- To produce a viable artifact in the form of a construct, a model, a method, or an instantiation.

• Problem relevance:- To develop technology-based solutions to important and relevant business problems.

• Design evaluation:- The utility, quality, and efficacy of a design artifact must be rigorously demonstrated via well-executed evaluation methods.

16

Research Methodology cont…

• Research contributions: - Provide clear and verifiable contributions in the areas of the design artifact, design foundations, and/or design methodologies.

• Research rigor:- Relies upon the application of rigorous methods in both the construction and evaluation of the design artifact.

• Design as a search process:- The search for an effective artifact requires utilizing available means.

• Communication of research:- Presents effectively both to technology-oriented as well as management-oriented audiences.

17

Research Questions• How to design an architecture that emerges for managing business-intelligence

generation with highly distributed, large data sets?• RQ1: How to design a conceptual architecture of the Business Intelligence

Management (BIM) system using multi-agent system (MAS)?• RQ2: How to assure effective access rights mechanisms during the

knowledge-sharing processes?• RQ3: How to support exception management and compensation mechanisms

when a knowledge-sharing process fails?

18

Research Questions

RQ1: How to design a conceptual architecture of the Business Intelligence Management (BIM) using multi-agent system (MAS)?

What is the requirement to design a conceptual BIM-architecture?

What kind of methodology is required for developing such a BIM system?

What is the purpose of AOM for assigning the roles and behavior of agents?

19

Research Questions cont…

RQ2: How to assure effective access rights mechanisms during the knowledge-sharing processes?

What type of communication protocol is required for interaction of agents?

What type of Management System is needed for knowledge-sharing processes of agents?

What is the impact of access right mechanism on BI-system?

20

Research Questions cont…

RQ3: How to support exception management and compensation mechanisms when a knowledge-sharing process fails?

Which kind of protocol and management system is needed to support exceptions?

Which type of domain failures are considered with agent-based BI-architecture?

What are the roles of agents in compensation mechanism processes?

21

Selection of Suitable Methodology

• The Gaia methodology is known as the first and complete methodology for the analysis and design of MAS [17, 18, 19].

• ROADMAP (Role-Oriented Analysis and Design for Multi-agent Programming) [19] – Emphasizes on domain and system analysis

• RAP/AOR (Radical Agent-Oriented Process /Agent-Object- Relationship) [19] – Emphasizes on design of system.

22

Domain Analysis and Detail Design

• We apply ROADMAP and RAP/AOR methodologies for analysis and design processes.

• To demonstrate the problem domain, we describe the functional requirement of BIM-architecture using concepts such as:

• Goal model

• Domain model

• Knowledge model

23

Domain Analysis and Detail Design cont..

• Identify research goals.

• Identify potential of agents and their capabilities.

• Identify operational constraints.

• Refine objects and agents actions.

• Identify alternative agents responsibilities.

• Assign roles to responsible agents.

• All other related protocols and algorithms require to BI-MAS.

24

Why goal model?

• Explain requirements and non-requirement functions which is required to BI-system.

• Used to assign responsibilities to agents.

• Provides basic information for detecting and resolving conflicts that arise from multiple viewpoints.

25

The notation for goal modelsMeaning Symbol

(Functional) Goal

(Non-functional) Quality Goal

Role

Relationship between goals

Relationship between goals and quality goals --------------------------

From reference [7]

26

Figure 2 : The goal model

27

Why domain model?

• Is a derivation of the environment and knowledge mode in ROADMAP methodology.

• Gives a conceptual framework of the domain problem.

• Represents:• Domain entities• Agent roles• Relationship between agents

28Figure 3: The domain model

29

Why knowledge model?

• Provides a framework of knowledge for agents of problem domain.

• Support to assign prior knowledge for each agent.

• Describe a playing role of agent that represents a new knowledge.

• Shows the knowledge about each agent and about objects in environment.

30

Figure 4 : The knowledge model

31

Architecture Overview and Behavior DefinitionIn the BI-MAS architecture, we specify an agent or a group of agents role in the system and perform certain tasks and functions that show the agent responsibilities.

• Stakeholder agent ( human agent/person)

• Scheduler agent

• Facilitator agent

• Miner agent

• Dispatcher agent

• Aggregator agent

• Evaluator agent

32 Figure 5: General architecture of the BI-MAS

33

Agent-based Communication Protocol and Model

• The communication protocols can be used to specify the policy that the agents follow in their interactions with each other.

• The interaction protocols are used to define a sequence of communication messages between the agents and describe how the agents should react during the interaction processes.

34

Agent-based Communication Protocol and Model cont…

• Citation [20] demonstrates about ten to twenty major standard initiatives that are applied in standardizing distributed system interoperability and FIPA (Foundation for Intelligent Physical Agents) is only one particular type of these.

• According to [21], Agent Petri Net (APN) is used to design FIPA interaction protocols between agents in distributed environments.

35

Figure 6 : The Request and Inform protocols of FIPA in BI-MAS

36

Conclusion and Future Work• We focus on developing an agent-based DDM architecture for the mining of

large datasets in a distributed heterogonous environment.

• In this context, we use the ROADMAP and RAP/AOR methodologies.

• We designed goal-, domain-, and knowledge models for proposed architecture

• The MAS-based DDM architectures can concern on more research to extend the design and to develop a system that is more flexible, adaptable, robust and easier with the use of comprehensive methodologies.

37

Future Activity

Months Activities on 2016 Remarks April, May, June and July

Lectures with Master and Bachelor students,

Supervising of 2 master students work on third Journal paper +

Experimental result using CPN and JADE

Attend to ICAAMS conference on June 13 - 14 in Italy

15 August – 15 September

Work on conference paper and search some related conference to submit

In Tallinn

October, November and December

Continue lectures with Master and Bachelor students, supervising of them, and continue to work on Journal paper

In Kabul

38

Future Activity

Months Activities on 2017 Remarks January, February, and March

Work 2nd Journal paper + Experimental result of my research work to complete the research work

In Tallinn

April, May and June

If we be in Tallinn, start to write the final defense thesis.

In Tallinn

July, August, and September

Finalize my PhD thesis May be In Kabul, I don’t know exactly

First of October Will defend my PhD research work In Tallinn

39

References[1] Kumar, A. K. Tyagi and S. K. Tyagi, “Data Mining : Various Issues and Challenges for Future a Short Discussion on Data Mining Issues for Future Work," International Journal of Emerging Technology and Advanced Engineering, vol. 4, no. 1, pp. 1-8, 2014.

[2] S. V. S. G. DEVI, "A Survey on Distributed Data Mining and Its Trends," International Journal of Research in Engineering & Technology (IMPACT: IJRET), vol. 2, no. 3, pp. 107-120, 2014.

[3] J. Silva, C. Giannella, R. Bhargava, H. Kargupta and M. Klusch, "Distributed data mining and agents," German Research Center for Artificial Intelligence, vol. 18, no. 7, pp. 791-807, 2005.

[4] Moemeng, X. Zhu and L. Cao, "Integrating Workflow into Agent-Based Distributed Data Mining Systems," Agents and Data Mining Interaction, vol. 5980, no. i, pp. 4-15, 2010.

[5] Loebbert and G. Finnie, "A Multi-Agent Framework for Distributed Business Intelligence Systems," Hawaii International Conference on System Sciences, pp. 4129-4137, 2012.

[6] Wei Wu, “Integrating Building Information Modeling and Green Building Certification: The BIM-Leed Application Model Development“ , retrieved from : http://etd.fcla.edu/UF/UFE0041603/wu_w.pdf

[7] Leon S. Sterling and Kuldar Taveter, “The Art of Agent-Oriented Modeling”, on 28.02.2016, retrieved from: http://aom.ttu.ee/

[8] March, S. T., Smith, G. F., (1995). Design and natural science research on information technology. Decision Support Systems, 15(4), pp. 251–266.

[9] von Alan, R. Hevner, et al. "Design science in information systems research." MIS quarterly 28.1 (2004): 75-105.

40

References[10] S. Roe, S. Vidyavathi and G. Ramaswamy, "Distributed Data Mining and Agent Mining Interaction and Integration : a Novel Approach," IJRRAS 4, vol. 4, pp. 388-398, 2010.

[11] M. O. Khozium, "Multi-Agent System Overview : Architectural Designing using Practical Approach," Internation Journal of Computers& Technology, vol. 5, no. 2, pp. 85-93, 2013.

[12] Loebbert and G. Finnie, "A Multi-Agent Framework for Distributed Business Intelligence Systems," Hawaii International Conference on System Sciences, 45th, pp. 4129-4137, 2012.

[13] H. Kargupta, I. Hamzaoglu and B. Stafford, "Scalable, Distributed Data Mining Using An Agent Based Architecture," Third International Conference on the Knowledge Discovery and Data Mining, pp. 211-214, 1997.

[14] R.Hemamalini and L. Mary, "An Analysis on Multi - Agent Based Distributed Data Mining System," International Journal of Scientific and Research Publications, vol. 4, no. 6, pp. 1-6, 2014.

[15] S. Rao, "MultiAgent-Based Distributed Data Minig : An Overview," Internation Journal of Review in Computing, no. 2076-3328, pp. 82-92, 2010.

[16] S. V. S. G. DEVI, "A Survey on Distributed Data Mining and Its Trends," International Journal of Research in Engineering & Technology (IMPACT: IJRET), vol. 2, no. 3, pp. 107-120, 2014.

[17] Zamboell, N. R. Jennings and M. Wooldridge, "Developing Multiagent Systems: The Gaia Methodology," ACM Transactions on Software Engineering and Methodology (TOSEM) , vol. 12, no. 3, pp. 317-370, 2003.

41

References[18] M. Wooldridge, N. R. Jennings and D. Kinny, "The Gaia Mthodology for Agent-Oriented Analysis and Design," JAAMAS, pp. 1-27, 2000.

[19] K. Taveter, "Towards radical agent-oriented software engineering processes based on AOR modelling," Agent-oriented methodologies, Idea Group Inc., pp. 277-316, 2005.

[20] S. Poslad, "Specifying Protocols for Multi-Agent System Interaction," ACM transaction on Autonomous and Adaptive System, vol. 2, no. 4, p. 25, 2007.

[21] Marzougui and K. Barkaoui, "Interaction Protocols in Multi-Agent Systems based on Agent Petri Nets Model," International Journal of Advanced Computer Science and Applications ( IJACSA), vol. 4, no. 7, pp. 166-173, 2013

[22] David Poole and Alan Mackworth, “Foundations of computational agents”, retrieved from http://artint.info/html/ArtInt_7.html

42

Any Question?