Minsoo Lee

An Internet-based negotiation server for e-commerce

Abstract. This paper describes the design and implementation of a replicable, Internet-based negotiation server for conducting bargaining-type negotiations between enterprises involved in e-commerce and e-business. Enterprises can be buyers and sellers of products/services or participants of a complex supply chain engaged in purchasing, planning, and scheduling. Multiple copies of our server can be installed to complement the services of Web servers. Each enterprise can install or select a trusted negotiation server to represent his/her interests. Web-based GUI tools are used during the build-time registration process to specify the requirements, constraints, and rules that represent negotiation policies and strategies, preference scoring of different data conditions, and aggregation methods for deriving a global cost-benefit score for the item(s) under negotiation. The registration information is used by the negotiation servers to automatically conduct bargaining type negotiations on behalf of their clients. In this paper, we present the architecture of our implementation as well as a framework for automated negotiations, and describe a number of communication primitives which are used in the underlying negotiation protocol. A constraint satisfaction processor (CSP) is used to evaluate a negotiation proposal or counterproposal against the registered requirements and constraints of a client company. In case of a constraint violation, an event is posted to trigger the execution of negotiation strategic rules, which either automatically relax the violated constraint, ask for human intervention, invoke an application, or perform other remedial operations. An Event-Trigger-Rule (ETR) server is used to manage events, triggers, and rules. Negotiation strategic rules can be added or modified at run-time. A cost-benefit analysis component is used to perform quantitative analysis of alternatives. The use of negotiation servers to conduct automated negotiation has been demonstrated in the context of an integrated supply chain scenario.

SPEEDING UP MATERIALIZED VIEW SELECTION IN DATA WAREHOUSES USING A RANDOMIZED ALGORITHM

A data warehouse stores information that is collected from multiple, heterogeneous information sources for the purpose of complex querying and analysis. Information in the warehouse is typically stored in the form of materialized views, which represent pre-computed portions of frequently asked queries. One of the most important tasks when designing a warehouse is the selection of materialized views to be maintained in the warehouse. The goal is to select a set of views in such a way as to minimize the total query response time over all queries, given a limited amount of time for maintaining the views (maintenance-cost view selection problem). In this paper, we propose an efficient solution to the maintenance-cost view selection problem using a genetic algorithm for computing a near-optimal set of views. Specifically, we explore the maintenance-cost view selection problem in the context of OR view graphs. We show that our approach represents a dramatic improvement in time complexity over existing search-ba...

HiCoMo: High Commit Mobile Transactions

We introduce a new mobile transaction model applicable to decisionmaking applications over aggregate data warehoused on mobile hosts. The model allows the aggregate data to be updated in disconnection mode, while guaranteeing a very high rate of commitment on reconnection. We name such transactions High Commit Mobile Transactions, or HiCoMo. At reconnectiontime, HiCoMos are analyzed and several base (fixed network) transactions are generated in order to bring the same effect upon the base tables from which the aggregates are derived. In this paper, we provide a formal definition for the concepts related to HiCoMos, and a transformation algorithm that is used to analyze them and generate base transactions. We provide a simple example scenario to demonstrate the usefulness of this transaction model. Finally, we compare the commit behavior of HiCoMos to that of the two-tier model using simulation and a mobile transaction benchmark drawn from an inventory application domain.

Personalized digital TV content recommendation with integration of user behavior profiling and multimodal content rating

This paper presents the novel development of an embedded system that aims at digital TV content recommendation based on descriptive metadata collected from versatile sources. The described system comprises a user profiling subsystem identifying user preferences and a user agent subsystem performing content rating. TV content items are ranked using a combined multimodal approach integrating classification-based and keyword-based similarity predictions so that a user is presented with a limited subset of relevant content. Observable user behaviors are discussed as instrumental in user profiling and a formula is provided for implicitly estimating the degree of user appreciation of content. A new relation-based similarity measure is suggested to improve categorized content rating precision. Experimental results show that our system can recommend desired content to users with significant amount of accuracy.

An information infrastructure and e-services for supporting Internet-based scalable e-business enterprises

The paper presents an information infrastructure for supporting Internet-based scalable e-business enterprises (ISEE). The information infrastructure is formed by a network of ISEE hubs, each of which has a number of replicable e-business servers providing various e-services to individuals and businesses. The servers are the implementations of a number of core technologies developed to facilitate collaborative e-business, including business event and rule management, active distributed objects, constraint satisfaction processing, and cost benefit analysis and selection. Using the e-services provided by these servers, other e-services such as constraint-based brokering, supplier selection, active business process management, and automated negotiation can be developed. Supply chain management is used as an example of collaborative e-business in the descriptions of these technologies and their implementations.

A Big Data Model Supporting Information Recommendation in Social Networks

As information systems are becoming sophisticated and mobile, cloud computing, social networking services are now very popular to people, the amount of data is rapidly increasing every year. Big data is data which should be analyzed by a company or an organization, but has not been tried to be analyzed or could not have been processed by current technology. In this paper, we introduce a big data model for recommender systems using social network data. The model incorporates factors related to social networks and can be applied to information recommendation with respect to various social behaviors that can increase the reliability of the recommended information. The big data model has the flexibility to be expanded to incorporate more sophisticated additional factors if needed. The experimental results using it in information recommendation and using map-reduce to process it show that it is a feasible model to be used for information recommendation.

Efficient recommendation methods using category experts for a large dataset

Abstract Neighborhood-based methods have been proposed to satisfy both the performance and accuracy in recommendation systems. It is difficult, however, to satisfy them together because there is a tradeoff between them especially in a big data environment. In this paper, we present a novel method, called a CE method, using the notion of category experts in order to leverage the tradeoff between performance and accuracy. The CE method selects a few users as experts in each category and uses their ratings rather than ordinary neighbors’. In addition, we suggest CES and CEP methods, variants of the CE method, to achieve higher accuracy. The CES method considers the similarity between the active user and category expert in ratings prediction, and the CEP method utilizes the active user’s preference (interest) on each category. Finally, we combine all the approaches to create a CESP method, considering similarity and preference simultaneously. Using real-world datasets from MovieLens and Ciao, we show that our proposal successfully leverages the tradeoff between the performance and accuracy and outperforms existing neighborhood-based recommendation methods in coverage. More specifically, the CESP method provides 5% improved accuracy compared to the item-based method while performing 9 times faster than the user-based method.

The IDEAL approach to Internet-based negotiation for e-business

With the emergence of e-business as the next killer application for the Web, automating bargaining-type negotiations between clients (i.e., buyers and sellers) has become increasingly important. With IDEAL (Internet based Dealmaker for e-business), we have developed an architecture and framework, including a negotiation protocol, for automated negotiations among multiple IDEAL servers. The main components of IDEAL are a constraint satisfaction processor (CSP) to evaluate a proposal, an Event-Trigger-Rule (ETR) server for managing and triggering the execution of rules which make up the negotiation strategy (rules can be updated at run-time to deal with the dynamic nature of negotiations), and a cost-benefit analysis to help in the selection of alternative strategies. We have implemented a fully functional prototype system of IDEAL to demonstrate automated negotiations among buyers and suppliers participating in a supply chain.

Intelligent dynamic workflow support for a ubiquitous Web service-based manufacturing environment

The ubiquitous environment is increasingly being considered as a platform for finding and integrating separate distributed services. Individuals or businesses can provide their services in the form of Web services in such a ubiquitous environment, and business processes that integrate such ubiquitous Web services can be formed using workflow technology. Workflow design and execution in such a dynamic and distributed environment needs to be very flexible in terms of incorporating changes. Web services may suddenly become unavailable and backup services may need to be found, or several providers of services may not be identifiable at the design time of the workflow. Therefore, dynamically finding and invoking Web services based on the workflow semantics need to be supported. BPEL4WS is the most popular and promising language among the workflow design languages for Web services. However, one of the problems with BPEL4WS is that it references the fixed WSDL file, which makes the workflow less flexible in dynamic and ubiquitous environments. Another problem is that it limits the amount of resources to only those that are specified. This makes it difficult to support semantics for finding similar or backup services in a ubiquitous network. This paper focuses on extending the BPEL4WS framework to include semantics by adding semantic constructs into WSDL and making use of ontologies in the BPEL4WS engine in order to support dynamic workflows suitable for ubiquitous environments.

On using category experts for improving the performance and accuracy in recommender systems

A variety of recommendation methods have been proposed to satisfy the performance and accuracy; however, it is fairly difficult to satisfy both of them because there is a trade-off between them. In this paper, we introduce the notion of category experts and propose the recommendation method by exploiting the ratings of category experts instead of those of the users similar to a target user. We also extend the method that uses both the category preference of a target user and his/her similarity to category experts. We show that our method significantly outperforms the existing methods in terms of performance and accuracy through extensive experiments with real-world data.