D-side: A facility and workforce planning group multi-criteria decision support system for Johnson Space Center

“To understand and protect our home planet, to explore the universe and search for life, and to inspire the next generation of explorers” is NASA's mission. The Systems Management Office at Johnson Space Center (JSC) is searching for methods to effectively manage the Center's resources to meet NASA's mission. D-Side is a group multi-criteria decision support system (GMDSS) developed to support facility decisions at JSC. D-Side uses a series of sequential and structured processes to plot facilities in a three-dimensional (3-D) graph on the basis of each facility's alignment with NASA's mission and goals, the extent to which other facilities are dependent on the facility, and the dollar value of capital investments that have been postponed at the facility relative to the facility's replacement value. A similarity factor rank orders facilities based on their Euclidean distance from Ideal and Nadir points. These similarity factors are then used to allocate capital improvement resources across facilities. We also present a parallel model that can be used to support decisions concerning allocation of human resources investments across workforce units. Finally, we present results from a pilot study where 12 experienced facility managers from NASA used D-Side and the organization's current approach to rank order and allocate funds for capital improvement across 20 facilities. Users evaluated D-Side favorably in terms of ease of use, the quality of the decision-making process, decision quality, and overall value-added. Their evaluations of D-Side were significantly more favorable than their evaluations of the current approach.     

A new algorithm for modeling online search behavior and studying ranking reliability variations

We design an information retrieval algorithm that mimics the stochastic behavior of decision-makers (DMs) when evaluating the alternatives displayed by an online search engine. The algorithm consists of a decision tree that incorporates all the 1024 decision nodes that may arise from the information retrieval process of DMs. We calibrate the behavior of the algorithm to the one observed from online users and run several sets of 1,000,000 queries. Each query lets DMs decide which subset of the ten alternatives composing the initial page of results to click, allowing us to evaluate their behavior as ranking reliability is assumed to decrease when DMs decide not to click on an alternative. We compare the click-through rates (CTRs) obtained when modifying the degree of ranking reliability derived from the alternatives displayed on the first page of search results. We illustrate how the stability of the CTR prevails among the top-ranked alternatives within relatively reliable scenarios while it drops when imposing large initial decrements in reliability. The resulting consequences regarding the importance of relative ranking positions are analyzed, the top three alternatives exhibiting a generally contained decrease in their CTRs that contrasts with the cumulative pattern arising from the fourth position onwards.

     

A robust optimization approach for imprecise data envelopment analysis

Crisp input and output data are fundamentally indispensable in traditional data envelopment analysis (DEA). However, the input and output data in real-world problems are often imprecise or ambiguous. Some researchers have proposed interval DEA (IDEA) and fuzzy DEA (FDEA) to deal with imprecise and ambiguous data in DEA. Nevertheless, many real-life problems use linguistic data that cannot be used as interval data and a large number of input variables in fuzzy logic could result in a significant number of rules that are needed to specify a dynamic model. In this paper, we propose an adaptation of the standard DEA under conditions of uncertainty. The proposed approach is based on a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set. Our robust DEA (RDEA) model seeks to maximize efficiency (similar to standard DEA) but under the assumption of a worst case efficiency defied by the uncertainty set and it’s supporting constraint. A Monte-Carlo simulation is used to compute the conformity of the rankings in the RDEA model. The contribution of this paper is fourfold: (1) we consider ambiguous, uncertain and imprecise input and output data in DEA; (2) we address the gap in the imprecise DEA literature for problems not suitable or difficult to model with interval or fuzzy representations; (3) we propose a robust optimization model in which the input and output parameters are constrained to be within an uncertainty set with additional constraints based on the worst case solution with respect to the uncertainty set; and (4) we use Monte-Carlo simulation to specify a range of Gamma in which the rankings of the DMUs occur with high probability.     

Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

The spatial and temporal variability of droughts were studied for the Northeast Algeria using SPI and RDI computed with monthly precipitation data from 123 rainfall stations and CFSR reanalysis monthly temperature data covering the period 1979–80 to 2013–14. The gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the time scales of 3-, 6- and 12-month with the same observed rainfall data. Spatial and temporal patterns of droughts were obtained using Principal Component Analysis in S-Mode with Varimax rotation applied to both SPI and RDI. For all time scales of both indices, two principal components were retained identifying two sub-regions that are similar and coherent for all SPI and RDI time scales. Both components explained more than 70% and 74% of drought spatial variability of SPI and RDI, respectively. The identified sub-regions are similar and coherent for all SPI and RDI time scales. The Modified Mann-Kendall test was used to assess trends of the RPC scores, which have shown non-significant trends for decreasing drought occurrence and severity in both identified drought sub-regions and all time scales. Both indices have shown a coherent and similar behavior, however with RDI likely showing to identify more severe and moderate droughts in the southern and more arid sub-region which may be due to its ability to consider influences of global warming. Results for RDI are quite uniform relative to time scales and show smaller differences among the various climates when compared with SPI. Further assessments covering the NW and NE of Algeria using longer time series should be performed to better understand the behavior of both indices.     

Consumer communications and networks: interest continued to grow - [guest editorial]

Presents four additional articles on the general topic of Consumer Communications and Networking. The first set of articles appeared in the December 2008 issue.     

Advances In Peer-To-Peer Content Search

Peer-to-peer (P2P) computer networks have recently received tremendous attention due to their inherent scalability and flexibility, which facilitates a broad spectrum of innovative multimedia applications. Such networks rely on the power of participant nodes of the network (called peers) for communications and computation. Traditional applications of P2P multimedia include decentralized file sharing and content distribution. Yet, the value of the virtually unlimited amount of data distributed in the P2P network will be sacrificed if effective and efficient ways to locate the content are missing. This challenge has stimulated extensive research in recent years, and many new P2P content search methods have been proposed. This paper provides a timely review of influential work in the area of peer-to-peer (P2P) content search. We begin with a survey of text-based P2P search mechanisms and continue with an exposition of content-based and semantic-based approaches followed by a discussion of future directions.     

An extended compromise ratio method for fuzzy group multi-attribute decision making with SWOT analysis

The technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi-attribute decision making (MADM) method that is used to identify the most attractive alternative solution among a finite set of alternatives based on the simultaneous minimization of the distance from an ideal solution (IS) and the maximization of the distance from the nadir solution (NS). We propose an alternative compromise ratio method (CRM) using an efficient and powerful distance measure for solving the group MADM problems. In the proposed CRM, similar to TOPSIS, the chosen alternative should be simultaneously as close as possible to the IS and as far away as possible from the NS. The conventional MADM problems require well-defined and precise data; however, the values associated with the parameters in the real-world are often imprecise, vague, uncertain or incomplete. Fuzzy sets provide a powerful tool for dealing with the ambiguous data. We capture the decision makers’ (DMs’) judgments with linguistic variables and represent their importance weights with fuzzy sets. The fuzzy group MADM (FGMADM) method proposed in this study improves the usability of the CRM. We integrate the FGMADM method into a strengths, weaknesses, opportunities and threats (SWOT) analysis framework to show the applicability of the proposed method in a solar panel manufacturing firm in Canada.     

Early detection and prevention of denial-of-service attacks: a novel mechanism with propagated traced-back attack blocking

A major threat to the information economy is denial-of-service (DoS) attacks. These attacks are highly prevalent despite the widespread deployment of perimeter-based countermeasures. Therefore, more effective approaches are required to counter the threat. This requirement has motivated us to propose a novel, distributed, and scalable mechanism for effective early detection and prevention of DoS attacks at the router level within a network infrastructure. This paper presents the design details of the new mechanism. Specifically, this paper shows how the mechanism combines both stateful and stateless signatures to provide early detection of DoS attacks and, therefore, protect the enterprise network. More importantly, this paper discusses how a domain-based approach to an attack response is used by the mechanism to block attack traffic. This novel approach enables the blockage of an attack to be gradually propagated only through affected domains toward the attack sources. As a result, the attack is eventually confined within its source domains, thus avoiding wasteful attack traffic overloading the network infrastructure. This approach also provides a natural way of tracing back the attack sources, without requiring the use of specific trace-back techniques and additional resources for their implementation.