Aligning Collaborative Business Processes—An Organization-Oriented Perspective

Business collaboration encompasses the coordination of information flows among organizations as well as the composition of their business processes toward mutual benefits. While integrating business processes of different organizations seamlessly, it brings great challenges to keep participating organizations as autonomous entities. To address this issue, we propose a new perspective on modeling collaborative business processes with a novel concept called relative workflow (RWF). With its visibility control mechanism, the RWF model defines what a participating organization can perceive in collaboration and thereby allows each organization to customize its own collaboration process and behaviors. In this paper, we present a formal definition of RWFs and related algorithms for generating RWFs. A prototype is implemented on the Web service platform for the proof-of-concept purpose.     

EvoOligo: Oligonucleotide Probe Design With Multiobjective Evolutionary Algorithms

Probe design is one of the most important tasks in successful deoxyribonucleic acid microarray experiments. We propose a multiobjective evolutionary optimization method for oligonucleotide probe design based on the multiobjective nature of the probe design problem. The proposed multiobjective evolutionary approach has several distinguished features, compared with previous methods. First, the evolutionary approach can find better probe sets than existing simple filtering methods with fixed threshold values. Second, the multiobjective approach can easily incorporate the user's custom criteria or change the existing criteria. Third, our approach tries to optimize the combination of probes for the given set of genes, in contrast to other tools that independently search each gene for qualifying probes. Lastly, the multiobjective optimization method provides various sets of probe combinations, among which the user can choose, depending on the target application. The proposed method is implemented as a platform called EvoOligo and is available for service on the Web. We test the performance of EvoOligo by designing probe sets for 19 types of Human Papillomavirus and 52 genes in the Arabidopsis Calmodulin multigene family. The design results from EvoOligo are proven to be superior to those from well-known existing probe design tools, such as OligoArray and OligoWiz.     

Quantitative analysis of location management and QoS in wireless networks

As a fundamental component in wireless networks, location management consists of two operations: location update and paging. These two supplementary operations enable the mobile user ubiquitous mobility. However, in case of failed location update, a significant consequence is the obsolete location identity in the network databases and thereafter the incapability in establishing the valid route for the potential call connection, which will seriously degrade the network quality-of-service (QoS). This issue is not theoretically studied in the literature. In this paper, we perform a quantitative analysis of the location management effect on QoS in the wireless networks. The metrics call blocking probability and the average number of blocked calls are introduced to reflect the QoS. For the sake of general applicability, the performance metrics are formulated with the relaxed tele-traffic parameters. Namely, the call inter-arrival time, cell residence time, location area residence time and location update inter-arrival time follow a general probability density function. The formulae are additionally specified in the static and several dynamic location management mechanisms. Numerical examples are presented to show the interaction between the performance metrics and location management schemes. The discussions on the sensitivity of tele-parameters are also given.     

A DR algorithm based on artificial potential field method

Considering player entity’s motion regularity into DR (Dead Reckoning) algorithm can improve its prediction accuracy in MMOG (Massively Multiplayer Online Games), a novel DR algorithm was proposed to solve this problem in this paper. First the artificial potential field model of player entities is created, and then the acceleration of player entities is weighted with the acceleration produced by the potential field force and the acceleration reckoned by the traditional DR algorithm. In order to calculate the weight, Q-Learning algorithm is used. The experiments show that the method can improve prediction accuracy and reduce the network traffic.     

Social Propagation: Boosting Social Annotations for Web Mining

This paper is concerned with the problem of boosting social annotations using propagation, which is also called social propagation. In particular, we focus on propagating social annotations of web pages (e.g., annotations in Del.icio.us). Social annotations are novel resources and valuable in many web applications, including web search and browsing. Although they are developing fast, social annotations of web pages cover only a small proportion (<0.1%) of the World Wide Web. To alleviate the low coverage of annotations, a general propagation model based on Random Surfer is proposed. Specifically, four steps are included, namely basic propagation, multiple-annotation propagation, multiple-link-type propagation, and constraint-guided propagation. The model is evaluated on a dataset of 40,422 web pages randomly sampled from 100 most popular English sites and ten famous academic sites. Each page’s annotations are obtained by querying the history interface of Del.icio.us. Experimental results show that the proposed model is very effective in increasing the coverage of annotations while still preserving novel properties of social annotations. Applications of propagated annotations on web search and classification further verify the effectiveness of the model.     

Flexible decision tree for data stream classification in the presence of concept change, noise and missing values

In recent years, classification learning for data streams has become an important and active research topic. A major challenge posed by data streams is that their underlying concepts can change over time, which requires current classifiers to be revised accordingly and timely. To detect concept change, a common methodology is to observe the online classification accuracy. If accuracy drops below some threshold value, a concept change is deemed to have taken place. An implicit assumption behind this methodology is that any drop in classification accuracy can be interpreted as a symptom of concept change. Unfortunately however, this assumption is often violated in the real world where data streams carry noise that can also introduce a significant reduction in classification accuracy. To compound this problem, traditional noise cleansing methods are incompetent for data streams. Those methods normally need to scan data multiple times whereas learning for data streams can only afford one-pass scan because of data’s high speed and huge volume. Another open problem in data stream classification is how to deal with missing values. When new instances containing missing values arrive, how a learning model classifies them and how the learning model updates itself according to them is an issue whose solution is far from being explored. To solve these problems, this paper proposes a novel classification algorithm, flexible decision tree (FlexDT), which extends fuzzy logic to data stream classification. The advantages are three-fold. First, FlexDT offers a flexible structure to effectively and efficiently handle concept change. Second, FlexDT is robust to noise. Hence it can prevent noise from interfering with classification accuracy, and accuracy drop can be safely attributed to concept change. Third, it deals with missing values in an elegant way. Extensive evaluations are conducted to compare FlexDT with representative existing data stream classification algorithms using a large suite of data streams and various statistical tests. Experimental results suggest that FlexDT offers a significant benefit to data stream classification in real-world scenarios where concept change, noise and missing values coexist.     

Broadcasting scheme with low client buffers and bandwidths for video-on-demand applications

Efficient data broadcasting is independent of request arrivals, and is thus highly promising when transmitting popular videos. A conventionally adopted broadcasting method is periodic broadcasting, which divides a popular video into segments, which are then simultaneously broadcast on different data channels. Once clients want to watch the video, they download the segments from these channels. The skyscraper broadcasting (SkB) scheme supports clients with small bandwidths. An SkB client requires only two-channel bandwidths to receive video segments. This work proposes a reverse SkB (RSkB) scheme, which extends SkB by reducing buffering spaces. The RSkB is mathematically shown to achieve on-time video delivery and two-channel client bandwidths. A formula for determining the maximum number of segments buffered by an RSkB client is presented. Finally, an analysis of RSkB reveals that its client buffer requirements are usually 25–37% lower than SkB. Extensive simulations of RSkB further demonstrate that RSkB yields lower client buffer demand than other proposed systems.

CoBTx-Net: A model for business collaboration reliability verification

Collaborative business process can become unreliable when business partners collaborate in a peer- based fashion without central control. Therefore, reliability checking becomes an important issue that needs to be dealt with for any generic solution in managing business collaboration. In this paper, we propose a novel Choreographical Business Transaction Net (CoBTx-Net) to model collaborative business process and to manage the collaboration by individual participants. Furthermore three reliability properties named Time-embedded dead marking freeness, Inter-organizational dead marking freeness, and Collaborative soundness are defined based on CoBTx-Net to verify (1) the violation of time constraint, (2) collaborative logic conflicts, and (3) the improper termination from individual organizations.

Computational simulation of Belousov–Zhabotinskii oscillating chemical reaction

The suitability of computational simulation of the Belousov–Zhabotinskii oscillating chemical reaction by differential kinetic methodology for resolving nonlinear multi-component system is demonstrated in this work. According to the Field–KÖrÖs–Noyes mechanism and the Oregonator model, the change of the concentrations of HBrO₂, bromide ion and cerium ion are simulated. The results of computational simulation are consistent with experimental results very well. At the same time, the effect of variables and parameters, especially the rate constant on the oscillation curve, are investigated deeply. A simple method of estimating rate constants is obtained through simulation the concentrations of key components of the system, and then comparison the simulation results with the experimental ones. The reasonable rate constant is also proposed.