Collaborative working: understanding mobile applications requirements

1 archivo PDF (4 páginas)The Rational Unified Process (RUP) as well as the related work embrace the importance of collaborative working teams during software development; but, user-interface designers and system analysts work in parallel or in sequential mode. However, this kind of relationship may not be effective, resulting on functional software but not meeting usability issues. Our proposal is that in order to understand mobile applications requirements work should be made collaboratively between analysts and user-interface designers through sharing artifacts like use-case scenarios, sketching and mock-up. In this paper, we propose a collaborative work framework to meet mobile applications requirements. Also, we show preliminary results of a case study to assess this approach. Results suggest that the collaborative team got a common understanding about system limits and functional and usability requirements.Universidad Autónoma Metropolitan

A criterion and incremental design construction for simultaneous kriging predictions

In this paper, we further investigate the problem of selecting a set of design points for universal kriging, which is a widely used technique for spatial data analysis. Our goal is to select the design points in order to make simultaneous predictions of the random variable of interest at a finite number of unsampled locations with maximum precision. Specifically, we consider as response a correlated random field given by a linear model with an unknown parameter vector and a spatial error correlation structure. We propose a new design criterion that aims at simultaneously minimizing the variation of the prediction errors at various points. We also present various efficient techniques for incrementally buillding designs for that criterion scaling well for high dimensions. Thus the method is particularly suitable for big data applications in areas of spatial data analysis such as mining, hydrogeology, natural resource monitoring, and environmental sciences or equivalently for any computer simulation experiments. The effectiveness of the proposed designs is demonstrated through numerical examples

Resource Allocation in Service Area based Networks

By applying joint transmission in the downlink and joint detection in the uplink, the novel service area architecture allows multiple mobile stations to be simultaneously active on the same OFDM subcarrier without causing interference to each other. Moreover, the proposed adaptive subcarrier and power allocation techniques are shown to be able to improve the spectral efficiency significantly in service area based networks. The significance of the frequency selectivity of wireless channels, the correlation among users’ spatial signatures and the presence of interferences to resource allocation is also assessed through simulations.Durch den Einsatz von Joint Detection in der Aufwärtsstrecke und Joint Transmission in der Abwärtsstrecke ermöglicht die neuartige Service Area Architektur es mehreren Mobilstationen in dem selben OFDM-Subträger gleichzeitig interferenzfrei aktiv zu sein. Darüber hinaus wrid gezeigt, dass die vorgeschlagenen adaptiven Subträger- und Leistungsallokationstechniken die spektrale Effizienz eines Service Area basierten Mobilfunksystems erheblich erhöhen können. Die Bedeutung der Frequnzselektivität der Funkkanäle, der Korrelation zwischen räumlichen Signaturen der Teinehmer und der Existenz der Interferenz für die adaptive Ressourcenallokation wird ebenfalls durch Computersimulationen bewertet

Instance selection of linear complexity for big data

Over recent decades, database sizes have grown considerably. Larger sizes present new challenges, because machine learning algorithms are not prepared to process such large volumes of information. Instance selection methods can alleviate this problem when the size of the data set is medium to large. However, even these methods face similar problems with very large-to-massive data sets. In this paper, two new algorithms with linear complexity for instance selection purposes are presented. Both algorithms use locality-sensitive hashing to find similarities between instances. While the complexity of conventional methods (usually quadratic, O(n2), or log-linear, O(nlogn)) means that they are unable to process large-sized data sets, the new proposal shows competitive results in terms of accuracy. Even more remarkably, it shortens execution time, as the proposal manages to reduce complexity and make it linear with respect to the data set size. The new proposal has been compared with some of the best known instance selection methods for testing and has also been evaluated on large data sets (up to a million instances).Supported by the Research Projects TIN 2011-24046 and TIN 2015-67534-P from the Spanish Ministry of Economy and Competitiveness

Computational estimate visualisation and evaluation of agent classified rules learning system

Student modelling and agent classified rules learning as applied in the development of the intelligent Preassessment System has been presented in [10],[11]. In this paper, we now demystify the theory behind the development of the pre-assessment system followed by some computational experimentation and graph visualisation of the agent classified rules learning algorithm in the estimation and prediction of classified rules. In addition, we present some preliminary results of the pre-assessment system evaluation. From the results, it is gathered that the system has performed according to its design specification

Metadata And Data Management In High Performance File And Storage Systems

With the advent of emerging e-Science applications, today\u27s scientific research increasingly relies on petascale-and-beyond computing over large data sets of the same magnitude. While the computational power of supercomputers has recently entered the era of petascale, the performance of their storage system is far lagged behind by many orders of magnitude. This places an imperative demand on revolutionizing their underlying I/O systems, on which the management of both metadata and data is deemed to have significant performance implications. Prefetching/caching and data locality awareness optimizations, as conventional and effective management techniques for metadata and data I/O performance enhancement, still play their crucial roles in current parallel and distributed file systems. In this study, we examine the limitations of existing prefetching/caching techniques and explore the untapped potentials of data locality optimization techniques in the new era of petascale computing. For metadata I/O access, we propose a novel weighted-graph-based prefetching technique, built on both direct and indirect successor relationship, to reap performance benefit from prefetching specifically for clustered metadata serversan arrangement envisioned necessary for petabyte scale distributed storage systems. For data I/O access, we design and implement Segment-structured On-disk data Grouping and Prefetching (SOGP), a combined prefetching and data placement technique to boost the local data read performance for parallel file systems, especially for those applications with partially overlapped access patterns. One high-performance local I/O software package in SOGP work for Parallel Virtual File System in the number of about 2000 C lines was released to Argonne National Laboratory in 2007 for potential integration into the production mode

Transmission congestion management by optimal placement of FACTS devices

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University, 13/09/2010.This thesis describes the implementation of the Flexible AC Transmission Systems (FACTS) devices to develop a market-based approach to the problem of transmission congestion management in a Balancing Market. The causes, remedies and pricing methods of transmission congestion are briefly reviewed. Balancing Market exists in markets in which most of the trading is done via decentralized bilateral contracts. In these markets only final adjustments necessary to ensure secure system operation is carried out at a centralized Balancing Market. Each market player can participate in the Balancing Market by submitting offers and bids to increase and decrease its initially submitted active generation output. In this research a method is proposed to reduce costs associated with congestion re-dispatch in a Balancing Market by optimal placement of FACTS devices, and in particular Thyristor Controlled Phase Shifter Transformers (TCPST). The proposed technique is applicable to both Mixed Integer Linear Programming (MILP) and Mixed Integer Non-Linear Programming (MINLP). In the MILP a power system network is represented by a simplified DC power flow under a MILP structure and the Market participants' offers and bids are also represented by linear models. Results show that applications of FACTS devices can significantly reduce costs of congestion re-dispatch. The application of the method based on the MINLP creates a nonlinear and non-convex AC OPF problem that might be trapped in local sub-optima solutions. The reliability of the solution that determines the optimal placement of FACTS devices is an important issue and is carried out by investigation of alternative solvers. The behavior of the MINLP solvers is presented and finally the best solvers for this particular optimization problem are introduced. The application of DC OPF is very common in industry. The accuracy of the DC OPF results is investigated and a comparison between the DC and AC OPF is presented