8027991
The Stakeholder Matrix: Supporting the Modeling of Responsibility in Situation-Oriented Directories
Situation-oriented directories help to determine the stakeholder responsible for a given situation. Defining all responsibility assignments for a directory can be challenging if situations need to be distinguished by many attributes and some of the attributes have many potential values. In this paper we present the stakeholder matrix, a compact representation of a situation-oriented directory that makes it easier for users to ensure the completeness and consistency of the assignments in the directory. Our evaluation showed that in a real world case the number of responsibility assignments could be compacted from 33700 to 128. We also review related research and show that compact representations similar to the stakeholder matrix will be useful to support validation of definitions in expert finding systems, content-based access systems and content-based routing systems.
9437535
Practical Application of FDC in Software Service Pricing
This paper proposes a modified fully distributed cost (FDC) pricing method with third-degree price discrimination for software service. The method is a feasible and simple method to be used in practice. The pricing process is generalized as involving data collection, strategy analysis and strategy establishment. Then a practical SCM software service pricing in retail industry is described
43377731
Software as a Service: An Integration Perspective
Software as a Service (SaaS) is gaining momentum in recent years with more and more successful adoptions. Though SaaS is delivered over Internet and charged on per-use basis, it is software application in essence. SaaS contains business data and logics which are usually required to integrate with other applications deployed by a SaaS subscriber. This makes Integration become one of the common requirements in most SaaS adoptions. In this paper, we analyze the key functional and non-functional SaaS integration requirements from an industry practitioner point of view; and summarize the SaaS integration patterns and existing offerings; then point out the gaps from both technology and tooling perspectives; finally we introduce a SaaS integration framework to address those gaps. Considering there is no much academic work on SaaS service modeling, we come up with a SaaS service description framework as an extension of Web Service description, so as to model SaaS unique features in a unified way. With the supported tooling and runtime platform, the framework can facilitate the SaaS integration lifecycle in a model-driven approach.
22845738
A Policy-Driven Approach for Software-as-Services Customization
Software as services (SaS) is emerging as a new application software delivery approach, and has extensively leveraged Web services and other SOA concepts and technologies. Compared to the traditional ones, SaS has the following distinctive features: application delivery over Internet, usage based pricing mechanism, etc. Potentially, this approach can significantly reduce the total cost of ownership of application software. However, it does have a number of unique issues that include service customization, multi-tenancy, complex billing model, etc. If not handled properly, these issues can result in ad-hoc and manual service consumption practices, which could eliminate the benefits of the SaS approach. In this paper, we are going to specifically deal with the service customization issue. To that end, we proposed a novel SaS customization policy and a supporting framework that is realized through a design-time tooling and a runtime environment. We also provided a case study to verify the effectiveness of the proposed approach.
11313782
Efficient Heuristics for Solving Probabilistic Interval Algebra Networks
A probabilistic interval algebra (PIA) network is an interval algebra network with probabilities associated with the labels on an edge. The probabilities on each edge sum to 1. A solution is a consistent scenario where the product of the probabilities associated with each unique edge label is maximized. In this paper we investigate previous PIA network solution algorithms, and propose new ones. Our first algorithm is based on best first search and guarantees to output the optimal solution. However, this algorithm is only feasible for toy problems. We augment the algorithm with three heuristics. Although our proposed algorithm does not guarantee an optimal solution, it is very useful in practice. Good solutions can be generated quickly
24105890
On the Impact of Carrier Frequency Offsets in OFDM/SDMA Systems
The combined OFDM/SDMA approach has raised lots of research interests recently as it appears to be quite suitable for future broadband wireless transmission. In order to build practical OFDM/SDMA systems, we need the performance evaluation of such systems under real-world conditions. The performance degradation exists of an OFDM/SDMA system due to carrier frequency offsets (CFO), because single-user plain OFDM is highly sensitive to CFO. However, the characteristic of the degradation is not clear. In this paper, the impact of CFO on the performance of an uplink OFDM/SDMA system is analyzed. Different from the existing analysis, we jointly consider the impact of CFO on channel estimation and multi-user signal detection. Analytical expressions are derived for the variance of channel estimation error and multi-user signal detection error in the presence of CFO, whose accuracies are validated by simulation results. Observations related to channel estimation accuracy and the interaction of the CFO effects from different users are also given.
15756929
Adaptive Orthonormal Random Beamforming and Multi-Beam Selection for Cellular Systems
Channel state information (CSI) at transmitter plays an important role for multiuser MIMO broadcast channels, but full CSI at transmitter is not available for many practical systems. Previous work has proposed orthonormal random beamforming (ORBF) [16] for MIMO broadcast channels with partial channel state information (CSI) feedback, and shown that ORBF achieves the optimal sum-rate capacity for a large number of users. However, for cellular systems with moderate number of users, i.e., no more than 64, ORBF only achieves slight performance gain. Therefore, we analyze the performance of ORBF with moderate number of users and total transmit power constraint and show that ORBF scheme is more efficient under low SNR. Then we propose an adaptive ORBF scheme that selects the number of random beams for simultaneous transmission according to the average signal-to-noise ratio (SNR). Moreover, a multi-beam selection (MBS) scheme that jointly selects the number and the subset of the multiple beams is proposed to further improve the system performance for low SNR cases. The simulation results show that the proposed schemes achieve significant performance improvement when the number of users is moderate.
17637228
Research on the Application of Industrial CT for Relics Image Reconstruction
The purpose of this paper is to visualizing the internal structure of historic relics by using the industrial computed-tomography (CT). The image reconstruction of CT scan data can be used to observe and analyze the antique inside that the eye has no way to distinguish without disruption. This paper have built the simulated testing model, produced the data of simulating projection, and accomplished the comparison of image reconstruction between of ART(SIRT) and FDK algorithm. After handling the real data of different materials, the experiment and analysis of CT image reconstruction have been carried out. This job has provided the effective technology support in the research of the heritage computed tomography scan platform. This paper also has provided the information for teaching and learning activities of cultural studies.
205396964
A distance-based separator representation for pattern classification
In pattern classification, Principle Component Analysis (PCA) and Linear Discriminate Analysis (LDA) are commonly used to reduce the dimensionality of input feature space. However, there exist some problems such that how many eigen vectors are needed to be the most effective in the transformation map as well as the lack of optimal separability in low dimensional data. In this paper, we present a new distance-based separator representation to solve these problems. The representation frame structure keeps adjustment pertaining to the problem complexity, and its dimensionality corresponds to the number of classes. Experimental results show that the new representation outperforms the PCA and LDA representations in multi-class classification and low-dimensional classification.