2979889
An effective customization procedure with configurable standard models
In electronic catalogs, commodities such as computers and electronic equipment are specified as standard models although a variety of possible alternative specifications can exist as a combination of selected options; therefore, customized configurations are essential to support various customers with individual needs. Thus, the problem here is the selection of a standard model and reconfiguration with this selected model. An issue is that requirements may be fulfilled by more than one standard model. To develop an algorithm that can find the near minimum price without causing unacceptable computation effort, we devised the Standard Model Selection and Modification (SMSM) Algorithm. To establish the SMSM Algorithm, we propose the Concurrent Local Propagation procedure complemented with pruning capability owing to the nature of standard models. The effective strategies for selection of seed variables and stopping rules are devised through comparative experiments.For the experiment, we use Dell's personal computer (PC) products consisting of 42 standard models with 25 specification variables. The SMSM Algorithm is tested with 75 experimental cases, and we found that the most similar standard models could discover the minimum price only in 24 out of 75 cases and that the SMSM procedure could reduce the price by 6.04% from the one offered by the most similar standard models.
6995681
Quasi-optimal case-selective neural network model for software effort estimation
Abstract A number of software effort estimations have attempted using statistical models, case based reasoning, and neural networks. The research results showed that the neural network models perform at least as well as the other approaches, so we selected the neural network model as the estimator. However, since the computing environment changes so rapidly in terms of programming languages, development tools, and methodologies, it is very difficult to maintain the performance of estimation models for the new breed of projects. Therefore, we propose a search method that finds the right level of relevant cases for the neural network model. For the selected case set, the scale of the neural network model can be reduced by eliminating the qualitative input factors with the same values. Since there exist a multitude of combinations of case sets, we need to search for the optimal reduced neural network model and corresponding case set. To find the quasi-optimal model from the hierarchy of reduced neural network models, we adopted the beam search technique and devised the case-set selection algorithm. We have shown that the resulting model significantly outperforms the original full model for the software effort estimation. This approach can be also used for building any case-selective neural network.
40201784
Case-based modification for optimization agents: AGENT-OPT
For the effective implementation of an inter-organizational supply chain on the Web, many optimization model agents need to be embedded in the distributed software agents. For instance, many suppliers make requests to a delivery scheduler who manages a model warehouse at the e-hub. The scheduler deals with the scheduling of many truckers and each trucker's agent must have its own routing optimization models. Since the formulations in the model warehouse vary depending upon the requirements, it is impossible to formulate all combinations in advance. Therefore, we need a case-based model modification scheme that can generate the required formulation from the semantically specified requirement in the agent communication language.This research deals with the issues of the architecture of an optimization model agent system AGENT-OPT, modeling request language in XML, optimization model representation in semantic-level objects using UNIK-OPT, a method of selecting a base model, an optimization model modification language (OMML), and rule-based modification reasoning. The approach is applied to the delivery scheduling to study the effect of base model selection policies on the modification effort. To determine whether to start with a primitive model, full model, or the most similar model, we experimented with the sensitivity of proximity to the primitive model on 24 cases and discovered the threshold for choosing the most efficient base model.
27969393
Least modification principle for case-based reasoning: a software project planning experience
A software project plan is composed of stages of activities and detailed tasks to be performed, and precedence restrictions among them. A project network is very complex and its construction requires a vast amount of field knowledge and experience. To assist the construction of a software project network, we adopt the case-based reasoning approach. However, the software project network may be designed differently depending upon the adopted development methodology and the style of the manager, so full automation of adjusting a past case is almost impossible. Thus, reducing the modification effort to a minimum is very important for enhancing the project planner's performance. In this research, we develop the framework of the Least Modification Principle (LMP) for Case-based Reasoning to solve this kind of problem. LMP is applicable when a reliable estimation of modification effort is possible. To apply the LMP for project network planning, we have selected 17 factors and the values for each factor to specify software projects. The modification effort is estimated based on the syntactic structure of modification rules. The performance of LMP is demonstrated with each of 31 test cases based on the other 30 past cases. We found that the LMP approach can significantly outperform the Ordinary Factor Matching approach.
7110658
Automatic Rule Generation by the Transformation of Expert's Diagram: LIFT
Abstract To enhance the efficiency of revealing and refining an expert's knowledge, the Expert's Diagram approach is proposed. The Expert's Diagram proposed in this paper is specifically designed for rule-based consulting systems. Using the Expert's Diagram approach, the knowledge acquisition system LIFT is developed, which transforms the Expert's Diagram automatically to rules in the syntax of the shell SKI 2 that is developed for tax consulting-purposes. For the transformation, either the conclusion-directed approach or the condition-directed approach can be applied. The role of LIFT can be generalized to some extent to adapt to the changes in the Expert's Diagram and target shells. According to our experience in the acquisition of Korean corporate tax knowledge, experts could reveal their knowledge effectively using the Expert's Diagram after a short period of training. Thus the rules could be generated automatically by LIFT without the aid of knowledge engineers.
6969387
Case- and Constraint-Based Project Planning for Apartment Construction
To effectively generate a fast and consistent apartment construction project network, Hyundai Engineering and Construction and Korea Advanced Institute of Science and Technology developed a case- and constraint-based project-planning expert system for an apartment domain. The system, FAS-TRAK- APT, is inspired by the use of previous cases by a human expert project planner for planning a new project and the modification of these cases by the project planner using his/her knowledge of domain constraints. This large-scale, case-based, and mixed-initiative planning system, integrated with intensive constraint-based adaptation, utilizes semantic-level metaconstraints and human decisions for compensating incomplete cases imbedding specific planning knowledge. The case- and constraint-based architecture inherently supports cross-checking cases with constraints during system development and maintenance. This system has drastically reduced the time and effort required for initial project planning, improved the quality and completeness of the generated plans, and is expected to give the company the competitive advantage in contract bids for new contracts.
12138219
Emergency response after 9/11: the potential of real-time 3D GIS for quick emergency response in micro-spatial environments
Terrorist attacks at the World Trade Center (WTC) in New York City and the Pentagon on September 11, 2001, not only affected multi-level structures in urban areas but also impacted upon their immediate environment at the street level in ways that considerably reduced the speed of emergency response. In this paper, we examine the potential of using real-time 3D GIS for the development and implementation of GIS-based intelligent emergency response systems (GIERS) that aim at facilitating quick emergency response to terrorist attacks on multi-level structures (e.g. multi-story office buildings). We outline a system architecture and a network data model that integrates the ground transportation system with the internal conduits within multi-level structures into a navigable 3D GIS. We examine important implementation issues of GIERS, especially the need for wireless and mobile deployment. Important decision support functionalities of GIERS are also explored with particular reference to the application of network-based shortest path algorithms. Finally, we present the results of an experimental implementation of an integrated 3D network data model using a GIS database of Franklin County, Ohio (USA). Our study shows that response delay within multi-level structures can be much longer than delays incurred on the ground transportation system, and GIERS have the potential for considerably reducing these delays.
7032611
Utility-Optimal Medium Access Control: Reverse and Forward Engineering
This paper analyzes and designs medium access control (MAC) protocols for wireless ad-hoc networks through the network utility maximization (NUM) framework. We first reverse-engineer the current exponential backoff (EB) type of MAC protocols such as the BEB (binary exponential backoff) in the IEEE 802.11 standard through a non-cooperative game- theoretic model. This MAC protocol is shown to be implicitly maximizing, using a stochastic subgradient, a selfish local utility at each link in the form of expected net reward for successful transmission. While the existence of a Nash equilibrium can be established, neither convergence nor social welfare optimality is guaranteed due to the inadequate feedback mechanism in the EB protocol. This motivates the forward-engineering part of the paper, where a network-wide utility maximization problem is for- mulated, using a collision and persistence probability model and aligning selfish utility with total social welfare. By adjusting the parameters in the utility objective functions of the NUM problem, we can also control the tradeoff between efficiency and fairness of radio resource allocation through a rigorous and systematic design. We develop two distributed algorithms to solve the MAC design NUM problem, which lead to random access protocols that have slightly more message passing overhead than the current EB protocol, but significant potential for efficiency and fairness improvement. We provide readily-verifiable sufficient conditions under which convergence of the proposed algorithms to a global optimality of network utility can be guaranteed, and through numerical examples illustrate the value of the NUM approach to the complexity-performance tradeoff in MAC design.
3126586
A Constant Propagation Algorithm for Explicitly Parallel Programs
In this paper, we present a constant propagation algorithm for explicitly parallel programs, which we call the Concurrent Sparse Conditional Constant propagation algorithm. This algorithm is an extension of the Sparse Conditional Constant propagation algorithm. Without considering the interaction between threads, classical optimizations lead to an incorrect program transformation for parallel programs. To make analyzing parallel programs possible, a new intermediate representation is needed. We introduce the Concurrent Static Single Assignment (CSSA) form to represent explicitly parallel programs with interleaving semantics and synchronization. The only parallel construct considered in this paper is cobegin/coend. A new confluence function, the π-assignment, which summarizes the information of interleaving statements between threads, is introduced. The Concurrent Control Flow Graph, which contains information about conflicting statements, control flow, and synchronization, is used as an underlying representation for the CSSA from.
18004950
Trajectory clustering: a partition-and-group framework
Existing trajectory clustering algorithms group similar trajectories as a whole, thus discovering common trajectories. Our key observation is that clustering trajectories as a whole could miss common sub-trajectories. Discovering common sub-trajectories is very useful in many applications, especially if we have regions of special interest for analysis. In this paper, we propose a new partition-and-group framework for clustering trajectories, which partitions a trajectory into a set of line segments, and then, groups similar line segments together into a cluster. The primary advantage of this framework is to discover common sub-trajectories from a trajectory database. Based on this partition-and-group framework, we develop a trajectory clustering algorithm TRACLUS. Our algorithm consists of two phases: partitioning and grouping. For the first phase, we present a formal trajectory partitioning algorithm using the minimum description length(MDL) principle. For the second phase, we present a density-based line-segment clustering algorithm. Experimental results demonstrate that TRACLUS correctly discovers common sub-trajectories from real trajectory data.
3162456
Predicting human interruptibility with sensors
A person seeking another person's attention is normally able to quickly assess how interruptible the other person currently is. Such assessments allow behavior that we consider natural, socially appropriate, or simply polite. This is in sharp contrast to current computer and communication systems, which are largely unaware of the social situations surrounding their usage and the impact that their actions have on these situations. If systems could model human interruptibility, they could use this information to negotiate interruptions at appropriate times, thus improving human computer interaction.This article presents a series of studies that quantitatively demonstrate that simple sensors can support the construction of models that estimate human interruptibility as well as people do. These models can be constructed without using complex sensors, such as vision-based techniques, and therefore their use in everyday office environments is both practical and affordable. Although currently based on a demographically limited sample, our results indicate a substantial opportunity for future research to validate these results over larger groups of office workers. Our results also motivate the development of systems that use these models to negotiate interruptions at socially appropriate times.