HICSS'01: Federated Data Mining Services and a ... - Semantic Scholar

Proceedings of the 34th Hawaii International Conference on System Sciences - 2001

Federated Data Mining Services and a Supporting XML-Based Language• S. Krishnaswamy1, A. Zaslavsky1, S.W. Loke2 School of Computer Science & Software Engineering, Monash University1 900 Dandenong Road, Caulfield East 3145, Australia Email: {shonali.krishnaswamy, arkady.zaslavsky}@csse.monash.edu.au CRC for Enterprise Distributed Systems Technology2 900 Dandenong Road, Caulfield East 3145, Australia Email: [email protected] Abstract E-businesses are increasingly looking towards data mining systems for meeting their business intelligence needs. However, the current state of the art in data mining does not allow any one system to be able to meet the diverse business intelligence needs of e-businesses. A second bottleneck is the high initial cost involved in establishing data mining infrastructure within an organisation. In this paper, we propose the concept of a federated data mining system hosted by an Application Service Provider (ASP) as a means of alleviating the bottlenecks of high cost and diverse data mining needs. We also present an XML DTD, which provides the basis for data mining systems to describe their services and architecture to the rest of the federation.

1. Introduction E-businesses are increasingly looking towards business intelligence tools to provide them with a competitive edge, by maximising the gain obtained from their information resources and supporting their strategic decision-making process. This demand for business intelligence by organisations operating in e-markets is driving data mining systems to support a wide variety of algorithms (to fulfil the diverse requirements of ebusinesses), to provide access to data mining services from web interfaces and even from mobile users with hand-held devices. Thus, in order for data mining systems to integrate well with the e-commerce world they need to

be able to operate in heterogeneous and distributed environments. Since the early 1990’s data mining systems have continued to evolve from stand-alone systems characterised by single algorithms with little support for the knowledge discovery process to integrated systems incorporating several mining algorithms, multiple users, various data formats and distributed data sources. We characterise the growth and evolution of data mining systems in a three-dimensional space as illustrated in figure 1. The orthogonal dimensions that specify the directions in which data mining systems are advancing include: complexity of data, levels of support and complexity of distribution. This continuous growth and evolution notwithstanding, the current state of the art in data mining systems makes it unlikely for any one system to be able to support the wide and varied business intelligence needs of e-markets. In this paper we propose a federation of data mining systems, as a means of meeting the diverse data mining needs of the e-commerce world. We also present Distributed Data Mining Systems – Markup Language (DDMS-ML), an XML DTD for distributed data mining systems participating in a federation to communicate with each other and share information about the services that they offer. DDMS-ML allows data mining systems to describe their respective functionality and architecture. It has been developed to be both generic and extensible in order to describe the various characteristics of distributed data mining systems. As an illustrative example, we use DDMS-ML to describe our Distributed Agent-based Mining Environment (DAME). The DAME system for

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THE WORK REPORTED IN THIS PAPER HAS BEEN FUNDED IN PART BY THE CO-OPERATIVE RESEARCH CENTRE PROGRAM THROUGH THE DEPARTMENT OF INDUSTRY, SCIENCE AND TOURISM OF THE COMMONWEALTH GOVERNMENT OF AUSTRALIA.

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Complexity of Data Organisations Spatial, temporal, multimedia systems Text Object-oriented Relational

Data Mining

Heterogeneous

ASCII Files Centralised, Distributed Stand-alone

Mobile, Distribution Ubiquitous

Automated Preprocessing Algorithm Selection Multiple Users Optimisation

Level of KD Support

Figure 1. Evolution of data mining systems distributed data mining is a hybrid architecture that integrates the mobile agent model with the client-server model and focuses on optimisation and cost-efficiency of the distributed mining process. This paper is organised as follows. In section 2, we illustrate scenarios of federated data mining systems and how they can benefit data mining in a B2B (business-to-business) context and C2B (customer-to-business) context. In section 3, we present DDMS-ML and discuss the components of its DTD. Section 4 presents a case study of a DDMS-ML document for the DAME distributed data mining system as a proofof-concept applicability of DDMS-ML. In section 5 we discuss related work. Finally in section 6 we conclude with the current status and future directions of our work.

2. Federated data mining systems The need for interoperability of data mining systems has been recognised and Microsoft’s OLE DB [7] initiative is a step in that direction. The primary motivation for interoperability of data mining systems is that users can benefit from the data mining services of several systems. Thus, a user who wants to mine data in a special format (e.g. say spatial or temporal data) can request this from a system that supports mining such data types. Similarly a user who is on the move but requires a data mining task may access a data mining system that specialises in meeting mobile users requirements. In this paper, we propose a federation of data mining systems, where each system can describe its functionality and services in a structured manner, as one alternative approach to achieving interoperability.

Consider an on-line shopping centre, which consists of buyers, dealers and a broker. The buyers access the shopping centre through a web-interface and interact with the vendors via the broker. The broker at one-level provides catalogue services to customers in terms of dealer profiles and availability of goods and services. At another level, the broker negotiates transactions between the buyers and the dealers. The need for distributed data mining in such a scenario arises from two possible sources, namely, the dealers and the broker. The dealers’ data mining requirements have their origins in traditional data mining applications such as market basket analysis. The broker’s data mining needs will be centred on customer-profiling to improve the level of service provided to individual customers. The environment is inherently distributed and heterogeneous. In addition to the complexity of distribution, e-commerce adds to the mining process an additional dimension of complexity by emphasising the importance of optimised response time. For example, in a situation where a product required by a customer is not currently available, the trader might want to provide the customer with details such as the likelihood of when the product would be available by analysing past trends or similar products offered by vendors. The trader might also want to give the customer the incentive for waiting by analysing dependencies with seasonal specials. In [5], we presented a framework whereby Application Service Providers (ASP) can host distributed data mining services with a framework for costing and billing for data mining services. The advantage of this is that it allows organisations to access data mining services without having to be concerned with the setting up costs. Similarly, the application service provider paradigm can

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be used to host a federation of data mining systems that ebusinesses can access to fulfil their data mining tasks. There are two models for hosting a federation of data mining systems using application service providers, namely, the customer-to-business (C2B) model and the business-to-business (B2B) model.

2.1 Customer to Business model (C2B) In this model, one ASP hosts a federated data mining system as illustrated in figure 2.

WEB-INTERFACE

E-CATALOG

On-line Shopping Centre BROKER

Database Dealer 1 Oracle

Dealer 2 Flat Files

Sybase

Legacy

F E D E R A T I O N M A N A G E R

Distributed Data Mining System 1



Application Service Provider

Figure 2. Federation of data mining systems hosted by an ASP Thus, several data mining systems are registered with one ASP who manages the federation and services the data mining needs of e-businesses. In this model the “customers” are the components of e-commerce systems such as the buyers, dealers and brokers and the “business” is the ASP which provides data mining services to its clients. The components of the figure are: Buyers. The buyers use the on-line shopping centre to procure goods and services. E-Commerce system. The e-commerce system provides the infrastructure for the on-line shopping centre. It comprises a web interface, an e-catalog, a broker and a database. The web interface is the point of access for the customers into the shopping centre. The “e-catalog” is a directory of the goods, services and dealer profiles. The broker negotiates transactions between the buyers and the dealers. The “database” is used to maintain transaction details, vendor and customer information for use by the ecatalog and the broker. Dealers. The dealers are the businesses that use the online shopping centre as a medium for marketing and selling their products. Application Service Providers (ASP). The ASP provides application services to the e-commerce system

components and the vendors. The focus in the above scenario is on the federated data mining service that is provided by the ASP. The dealers and the broker requiring this service pay the ASP for accessing the data mining systems that form the federation. The ASP must have an infrastructure for costing and billing clients for data mining services. Federated Data Mining System. The federated data mining system consists of several distributed data mining systems and a federation manager. The DDM systems support different functionality and special features that are tailored for specific data mining tasks in a distributed and heterogeneous environment. Several DDM systems have been proposed including Papyrus [9], JAM [10], DecisionCentre [1], Bodhi [4], 1998), IntelliMiner [8] and DAME [5]. The federation manager maintains information about the different data mining systems that are part of the federation such as the services provided. In this paper, we present an XML DTD called Distributed Data Mining Systems-Markup Language (DDMS-ML), which facilitates this information to be obtained from the individual DDM systems. Each system presents information about itself to the federation in the form of XML documents that conform to the DTD, which forms the basis for the federation manager to locate a DDM system that is best suited to perform a given task. The tags and the document type definition for DDMS-ML documents are presented in section 3 of this paper.

2.2 Business to Business model (B2B) In this model there are several ASPs which provide data mining services as illustrated in figure 3. The ASPs participate in a federation consisting of their individual data mining systems. Each ASP has its own customers. Distributed Data Mining System X

Application Service Provider #1

WEB-INTERFACE E-CATALOG On-line Shopping Centre

Dealer 1 Oracle

Distributed Data Mining System Y

BROKER

Database

Flat Files

Distributed Data Mining System Z

Dealer 2 Sybase

Legacy

Federation Manager



Figure 3. Federation of data mining systems hosted by several ASPs

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When an ASP receives a data mining request that it is unable to carry out it propagates this request onto the federation. This scheme can be viewed as a B2B model since each ASP is a business and it out-sources those requests from its clients that it does not have the resources to perform. The federation manager as before maintains profiles of the data mining systems participating in the federation in the form of DDMS-ML documents and coordinates the interaction between the different ASPs.

2.3 Federation Issues Managing a federation of distributed data mining systems involves several issues. The federation must provide the infrastructure for the following: • Registration. Data mining systems must be able to register and de-register themselves to join and leave the federation respectively. • Access and Usage. By joining a federation, the data mining system must provide access for the federation (and its users). Thus, it must allow the federation to use its services and must provide the federation with information about access and usage. • Communication Protocols. The protocols for communication and interaction between the federation and the data mining systems need to be specified. • Billing. Data mining systems must have the means for billing users for their services. In the C2B model, this is not as significant an issue since the ASP bills clients for data mining services. However, in the B2B model, when one ASP decides to outsource a task to another ASP, this raises issues such as the federation’s ability to find a data mining system that will provide a service for the lowest price. Thus, the federation must allow the participating data mining systems to quote what they will charge and then choose the best offer. • Security and Trust. The federation must provide good security for its clients so that they trust the federation with their data mining tasks. Federated data mining systems are a new concept and the above issues remain open for examination. The focus of this paper is to present an XML DTD that allows a data mining systems to describe their services and infrastructure to the rest of the federation.

3. Distributed Data Mining Systems – Markup Language (DDMS-ML) DDMS-ML is an XML DTD for distributed data mining systems that are part of a federation to describe their functionality, features and structure, and exchange

information about their respective architectures and services. In this section, we present the DTD for DDMSML documents. XML is widely used in metadata standards such as Microsoft’s Channel Definition Format (CDF) and is suitable for standardised information interchange in many domain specific contexts [3]. We believe that XML provides a suitable basis for DDMSML because of the following reasons: • XML query languages can be used to query a collection of XML documents [13]. Thus, the federation manager can query a collection of DDMSML documents to locate a DDM system in the federation that provides a particular service. • XML is humanly readable. • The Document Object Model (DOM) allows access to XML documents from within programming languages. • XSLT (Extended Style Sheet Language Transformation) [11] allows XML documents to be converted to other languages such as HTML, WML. We now discuss the structure and components of DDMS-ML documents. A DDMS-ML document consists of seven distinct parts, which are discussed as follows. Meta Information. This part of a DDMS-ML document contains information about the DDM system such as its name, version, date of development, organisation and developer. We impose the constraint that within a federation each DDM system must have a unique name. Connection and Access. This component of a DDMSML document provides information about the DDM server and how the federation can access it. The DDM server’s host-name, IP address and the usernames and passwords for use by the federation (including optional instructions for connection) are provided. We allow for a DDM system to be hosted by more than one server and for the federation to have one or more accounts on any server. Computational Resources. This component includes information about the DDM system’s computational resources. A DDM system can have several servers at its disposal. A server is either a stand-alone system or a parallel server or a cluster. A server may or may not be dedicated for distributed data mining. A server’s physical configuration such as the operating system, the CPU, the memory and the number of nodes (if the server is a cluster) is recorded. This information allows the federation manager to preliminarily determine a system’s relative suitability for a given task. Architectural Model. This component of a DDMS-ML document states whether the DDM system uses the clientserver approach, the mobile agent paradigm or a hybrid model for distributed data mining. This information is important in situations where a client requires a particular

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architectural model. For instance, a situation where the data to be mined is sensitive and the client does not want the data to be transported to the DDM server will warrant that a DDM system that uses mobile agents be used. It is also possible then that the mobile agent performs its task and is destroyed and not allowed to leave the site to provide further protection to the client. Data Types. This part of a DDMS-ML document states the data types that can be mined using a given DDM system. The following options have been specified: text, relational, spatial, temporal, image, video, multimedia, object-oriented and hypertext. However to allow for flexibility and extensibility the DTD allows specifying other data formats apart from the ones listed above. Specialisations and Features. This section of the document allows DDM systems in the federation to describe their distinguishing functions and special services. Similar to the data types, the DTD has some prespecified options such as support for parallel algorithms, optimisation, cost-efficiency, pre-processing, mobile users and visualisation. However, it also allows a DDM

system to present any other special features that it may possess. This component also includes information about support for “knowledge integration” which is the process of integrating results obtained from distributed data sets. Algorithms. This component of a DDMS-ML specifies the mining algorithms that are supported by the DDM system. The specification includes details such as the algorithm’s name, version and developer. This is followed by details regarding the structure of the input file for the algorithm, the input parameters, the command to call the algorithm and output model produced. The current version of DDMS-ML only allows the specification of a text input file. We are working on a format for specifying relational data. Specifying the structure of complex data files is a non-trivial task and is not part of our current focus. For such input data, the DTD only allows specifying the data type and the respective file extension required. This component of the document allows the federation manager to present details about algorithm usage to clients who might wish to use a particular system. The DTD for DDMS-ML is illustrated in figure 4 as follows:

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memory CDATA #REQUIRED >

Figure 4. Document Type Definition for DDMS-ML certain structure that provides for obtaining the necessary The above DTD has been designed to allow DDM information for a federation of data mining systems to systems to describe themselves in a flexible but structured function. manner. The DTD can be extended to incorporate other information such as interfaces and gateways. Its current 4. Case Study status is reflective of its applicability to our distributed The Distributed Agent-based Mining Environment data mining system DAME. The DTD is expressive to the (DAME) is a hybrid model for distributed data mining, extent that it allows DDM systems to describe their which integrates the client-server and mobile agent distinguishing features and at the same time it enforces a

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paradigms. The system has evolved to address the issue of cost-efficient distributed data mining. A detailed discussion of the DAME architecture and cost models for optimisation of the distributed data mining process can be found in [6]. In this section, we describe DAME using DDMS-ML to illustrate the applicability of the markup language. We first present an overview of the DAME architecture and then present a DDMS-ML document that specifies this system.

4.1 Overview of the DAME Architecture In this section we present a brief overview of the DAME system. The components of the DAME architecture illustrated in figure 5 are as follows: Users. The users request data mining services by connecting to the distributed data mining server. The users communicate with the distributed data mining management system through the user manager component. Dedicated Distributed Data Mining Server. This is a server with high computational power that acts as both the point of control for the distributed data mining process and the provision of dedicated resources for mining. The server maintains the distributed data mining management system. Distributed Data Mining Management System (DDMMS). The DDMMS is the software that performs the various tasks associated with the distributed data mining process. The DDMMS forms the core of this architecture and the way it is structured encapsulates the framework for

resource optimisation. The components within the DDMMS are a user manager, algorithm manager, optimiser, mining process manager and an agent control centre. We now present a detailed outline of the functionality of each of these sub-components. User Manager. The users connect to the distributed data mining system through the user manager. The user manager performs the following functions: authentication of users, profiling of the data mining task in terms specifying the user requirements including the data mining query, the output required, the time frame within which the output is required and supporting mobility of users by providing results and updates to users who are on the move and may not remain connected throughout the duration of the data mining process. Optimiser. The optimiser is the component that is primarily responsible for building an estimated cost of alternative strategies and determining the best option for performing the data mining task to meet user needs. The optimiser interacts with the mining process manager in order to collect statistics regarding the current status of the communication channels and the task profile (specifically to determine the user requirements for task completion and the algorithm allocated for the task). It also interacts with the agent control centre (i.e. the mine sweeper agent) for details regarding the data set size. Using the data collected by the mine sweeper and the mining process manager, the optimiser builds an estimated cost model for the alternative ways to perform the data mining and decides on the option that will meet the user requirements as closely as possible.

USERS

Result

Notebook

Workstation

PC

DDM Mangement System O p t i m i s e r

Algorithm Manager

User Manager

Mining Process Manager Knowledge Integrator

Mine Sweeper Agent

User Agent

Agent Control Centre

Local Computational Resources

Mining Agent

''06HUYHU Resource Monitoring Agents

Data Server 1

Network Monitoring Agent

Data transfer for mining locally

Data Server2

Client Server Model

Status Monitoring Information

Mobile Agent Model

Figure 5. DAME architecture

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Algorithm Manager. The algorithm manager’s primary task is to maintain the data mining algorithms that are part of the distributed data mining system. Users can register any mining algorithm with the system. The users can choose to make available the algorithms that they have registered to other users. At the time of incorporating an algorithm into the system, the algorithm manager records meta level information about the algorithm and its characteristics such as name, version, input parameters, operating environment and output produced. The algorithm manager feeds this information to the mining process manager, which maintains profiles about algorithmic characteristics. Mining Process Manager. This module forms the core of the distributed data mining system. It is basically the coordinating facility and the directory service for the different components of the system. The other components in the system access the mining process manager as it forms a point of reference from which information can be obtained regarding the current status of various aspects of the system. To the best of our knowledge, the mining process manager is the first integrated attempt in dynamically tracking and specifying the components and their interactions within the distributed data mining framework. Broadly, the components whose states and status are relevant include users, data mining tasks, data mining servers, data servers, communication resources and algorithms. It is obvious that the status of the information recorded must be current at all times. Agent Control Centre (ACC). The agent control centre is the framework within which the agent activities in the distributed data mining system take place. The ACC is

responsible for activating/generating/assembling agents required for the data mining process. It interacts closely with the DDMMS, particularly with the Mining Process Manager. The ACC activates a distributed data mining task on the basis of instructions received from the DDMMS. The optimiser determines the appropriate model for mining. If this model involves mobile agents, then the ACC is responsible for controlling and coordinating the agents that perform the task. When the mining model to be deployed is client-server then the mining agents are instructed to perform the task locally. It also provides the monitoring and status information from the network and the data sources to the Mining Process Manager. The different agent types in the system are user agents, network monitoring agents, data resource monitoring agents, mine-sweeper agents and miner agents.

4.2 DAME System in DDMS-ML We now present a DDMS-ML document that describes the DAME architecture presented in section 4.1. The DAME system is currently being implemented in Java and uses the WEKA package of data mining algorithms [12]. The current implementation only includes the ID3 algorithm which mines text data in the ARFF file format [12]. The DAME system has three servers being used currently with the following operating systems: Solaris, Linux and NT. The following DDMSML document illustrated in figure 6 represents the current status of DAME.

muruga.csse.monash.edu.au

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java -mx100000000 weka.classifiers.Id3 Decision Tree

Figure 6. DDMS-ML document for the DAME architecture

5. Related Work Federated data mining systems are just emerging and have not been presented in the literature. However, there has been a recent effort to develop data mining standards [7] and move towards open data mining systems. The inspiration for DDMS-ML comes primarily from the

Predictive Model Markup Language (PMML) [2], which is a markup language for describing the models output from data mining systems such as decision-trees and regression models. While PMML describes predictive models that are the results of data mining, DDMS-ML describes distributed data mining systems in terms of their architectural models, access, usage and services. The

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question of how DDMS-ML and PMML (since the two languages describe different aspects of distributed data mining) can be used together is an issue that needs to be further examined.

6. Conclusions and Future Directions In this paper we presented the architecture of federated data mining systems to support the business intelligence needs of e-commerce environments. A federated data mining system satisfies the diverse needs of e-businesses, but also alleviates the need to establish an expensive data mining infrastructure. E-businesses can thus benefit from a variety of data mining systems by subscribing to a federation without having to bear the burden of the initial set-up cost of data mining technology. We also presented DDMS-ML, an XML DTD that allows distributed data mining systems participating in a federation to describe their structure and functionality. Our immediate focus is to develop case studies of DDMS-ML documents for other distributed data mining systems and extend the DTD to allow specification of a wide spectrum of DDM systems. We are also working on addressing the different issues that arise in a federated data mining system.

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XML

[4] Kargupta,H., Park,B., Johnson,E., Riva Sanseverino,E., Di Silvestre,L., and Hershberger,D., (1998), “Collective Data Mining From Distributed Vertically Partitioned Feature Space”,

in KDD-98 Workshop on Distributed Data Mining, New York, USA, AAAI Press. [5] Krishnaswamy,S., Zaslavsky,A., and Loke,S,W., (2000a), “An Architecture to Support Distributed Data Mining Services in E-Commerce Environments”, Proceedings of the Second International Workshop on Advanced Issues in E-Commerce and Web-Based Information Systems, San Jose, Californinia, June 8-9, pp.238-246. [6] Krishnaswamy,S., Loke,S,W., and Zaslavsky,A., (2000b), “Cost Models for Heterogeneous Distributed Data Mining”, Proceedings of the Twelfth International Conference on Software Engineering and Knowledge Engineering, Chicago, Illinois, July 6-8, pp.31-38.

[7] Microsoft OLE DB for Data Mining, URL: http://www.microsoft.com/data/oledb/dm.htm March, 2000. [8] Parthasarathy,S., and Subramonian,R., (1999), “Facilitating Data Mining on a network of workstations”, to appear in Advances in Distributed Data Mining, (eds) H. Kargupta and P.Chan, AAAI Press. [9] Ramu,A.T., (1998), “Incorporating Transportable Software Agents into a Wide Area High Performance Distributed Data Mining Systems”, Masters Thesis, University of Illinois, Chicago, USA. [10] Stolfo,S.J., Prodromidis,A.L., Tselepis,L., Lee,W., and Chan,P.K., (1997), “JAM: Java Agents for Meta-Learning over Distributed Databases”, in Proc. of the 3rd Int. Conf. on Data Mining and Knowledge Discovery (KDD-97), Newport Beach, California, (eds) D.Heckerman, H.Mannila, D.Pregibon, and R.Uthurusamy, AAAI Press, pp. 74-81. [11] W3C XSL Transformations V1.0, URL:http://www.w3.org/TR/xslt , 1999. [12] Witten,I.H., and Eibe,F., (1999), “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”, Morgan Kauffman.

[13] XML Query Languages, URL:

http://www-db.research.belllabs.com/user/simeon/xquery.html , 1999.

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