Cheng-Ming Huang

LPT scheduling for fuzzy tasks

Scheduling mainly concerns allocating resources to tasks over time, under necessary constraints. In the past, the processing time for each task was usually assigned or estimated as a fixed value. In many real-world applications, however, the processing time for each job may vary dynamically with the situation. In this paper, fuzzy concepts are utilized in the LPT algorithm for managing uncertain scheduling. Given a set of tasks, each with its membership function for the processing time, the fuzzy LPT algorithm can get a scheduling result with a membership function for the final completion time. Also, the conventional LPT scheduling algorithm is shown as a special case of the fuzzy LPT scheduling algorithm with special membership functions being assigned. The fuzzy LPT scheduling algorithm is then a feasible solution for both deterministic and uncertain scheduling.

Linguistic object-oriented web-usage mining

Web mining has become a very important research topic in the field of data mining due to the vast amount of world wide web services in recent years. The fuzzy and the object concepts have also been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy object-oriented web mining algorithm to derive fuzzy knowledge from object data log on web servers. Each web page itself is thought of as a class, and each web page browsed by a client is thought of as an instance. Instances with the same class (web page) may have different quantitative attribute values since they may appear in different clients. The proposed fuzzy mining algorithm can be divided into two main phases. The first phase is called the fuzzy intra-page mining phase, in which the linguistic large itemsets associated with the same classes (pages) but with different attributes are derived. Each linguistic large itemset found in this phase is then thought of as a composite item used in phase 2. The second phase is called the fuzzy inter-page mining phase, in which the large sequences are derived and used to represent the relationship among different web pages. Both the intra-page linguistic association rules and inter-page linguistic browsing patterns can thus be easily derived by the proposed algorithm at the same time. An example is given to illustrate the proposed algorithm. Experimental results also show the effects of the parameters used in the algorithm.

Discovering mobile users' moving behaviors in wireless networks

Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to discover personal mobility patterns for helping systems provide personalized service in a wireless network. The classification and the duration of each location area visited by a mobile user are used as important attributes in representing the results. A data mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways. Experiments are also made to show the effect of the proposed algorithm.

A fuzzy LPT algorithm for scheduling

Scheduling is an important process widely used in the fields of manufacturing, production, management, computer science, and so on. It mainly concerns the allocation of resources to tasks over time, under some necessary constraints. In the past, the processing time of each job was usually assigned or estimated as a fixed value. In many real-world applications, however, the processing time of each job may dynamically vary with the situations. In this paper, the fuzzy concept is utilized in the longest processing time first (LPT) algorithm for managing uncertain scheduling. Given a set of tasks, each with its membership function for the processing time, the fuzzy LPT algorithm can get a scheduling result with a membership function for the final completion time. Also, the traditional LPT scheduling algorithm is shown as a special case of the fuzzy LPT scheduling algorithm with special membership functions being assigned. The fuzzy LPT scheduling algorithm is then a feasible solution for both the deterministic and uncertain scheduling.

LPT scheduling on fuzzy tasks with triangular membership functions

We (1998) demonstrated how discrete fuzzy concepts can easily be used in the LPT (longest processing time first) algorithm for managing uncertain scheduling. This paper extends application to the continuous fuzzy domain. The membership functions of the scheduled tasks are assumed to be triangular, and three heuristic fuzzy LPT scheduling methods are proposed to yield scheduling results with polygonal or triangular final completion-time membership functions. Experiments with different numbers of scheduled tasks are also presented to show the effectiveness of the proposed methods.

Discovering fuzzy inter- and intra-object associations

Research highlights? This paper proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. ? The proposed algorithm is divided into two main phases, one for intra-object linguistic association rules, and the other for inter-object linguistic association rules. ? The numbers of fuzzy intra-object association rules are usually smaller than those of fuzzy inter-object association rules because the attribute number is less than the item number in real applications. Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the fuzzy and the object concepts have been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. Each linguistic large itemset found in this phase is thought of as a composite item used in phase 2. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. An example is used to illustrate the algorithm. Experimental results are also given to show the effects of the proposed algorithm.

Discovering fuzzy personal moving profiles in wireless networks

Wireless networks and mobile applications have grown very rapidly and have made a significant impact on computer systems. Especially, the usage of mobile phones and PDA is increased very rapidly. Added functions and values with these devices are thus greatly developed. If some regularity can be known from the user mobility behavior, then these functions and values can be further expanded and used intelligently. This paper thus attempts to discover fuzzy personal mobility patterns for helping systems provide personalized service in a wireless network. The arrival time and the duration time of each location area visited by a mobile user are used as important attributes in representing the results. Since both the arrival time and the duration time are numeric, fuzzy concepts are used to process them and to form linguistic terms. A fuzzy mining algorithm has then been proposed, which is based on the AprioriAll algorithm, but different from it in several ways. The difference causes a delicate consideration in the design of the algorithm. An example is also given to demonstrate the algorithm. The linguistic representation of personal mobility patterns will be more natural and understandable for the system managers to provide better personalized service in a wireless network.

Mining knowledge from object-oriented instances

Abstract Data mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Recently, the object concept has been very popular and used in a variety of applications, especially for complex data description. This paper thus proposes a new data-mining algorithm for extracting interesting knowledge from transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different attribute values since they may appear in different transactions. The proposed algorithm is divided into two main phases, one for intra-object association rules, and the other for inter-object association rules. Two apriori-like procedures are adopted to find the two kinds of rules. The first phase finds out the association relation within the same kind of objects. Each large itemset found in this phase can be thought of as a composite item used in phase 2. The second phase then finds the relationship among different kinds of objects. Both the intra-object and inter-object association rules can thus be easily derived by the proposed algorithm at the same time. Experiments are also made to show the effect of the proposed algorithm.

Simultaneously Mining Fuzzy Inter- and Intra-Object Association Rules

The paper proposes a new fuzzy data-mining algorithm for extracting interesting knowledge from quantitative transactions stored as object data. Each item itself is thought of as a class, and each item purchased in a transaction is thought of as an instance. Instances with the same class (item name) may have different quantitative attribute values since they may appear in different transactions. The proposed fuzzy algorithm can be divided into two main phases. The first phase is called the fuzzy intra-object mining phase, in which the linguistic large itemsets associated with the same classes (items) but with different attributes are derived. The second phase is called the fuzzy inter-object mining phase, in which the large itemsets are derived and used to represent the relationship among different kinds of objects. Experimental results also show the effects of the proposed algorithm.

Linguistic Object-Oriented Web Mining

The paper proposes a new fuzzy object-oriented Web mining algorithm to derive fuzzy knowledge from object data log on Web servers. Each Web page itself is thought of as a class, and each web page browsed by a client is thought of as an instance. Instances with the same class (Web page) may have different quantitative attribute values since they may appear in different clients. The proposed fuzzy mining algorithm can be divided into two main phases. The first phase is called the fuzzy intra-page mining phase, in which the linguistic large itemsets associated with the same classes (pages) but with different attributes are derived. The second phase is called the fuzzy interpage mining phase, in which the large sequences are derived and used to represent the relationship among different Web pages. Experimental results also show the effects of the parameters used in the algorithm