In this inaugural issue of the journal on information systems, it is appropriate to reflect on some of the main challenges in this field and some of the desired topics that publishing authors will investigate. Computers, including phones, sensors and scientific instruments, and networks have brought revolutionary information technologies to create a digital space whose impact on humanity is being felt everywhere. Currently, over half a billion people log into Facebook to communicate with their contacts. They exchange more than 300 million photos and more than 3 billion votes and comments each day. Every server, device and system generates an everchanging stream of information in social media, business and scientific applications. These phenomena will accelerate over time with even richer and more heterogeneous information. In this environment, there is need for analytics to make sense of the volume, diversity, complexity, uncertainty of this data that is being generated at a fast pace. The main challenge will be to design and implement feature-extraction systems that can label media streaming such as images and videos; free-form tweets; text messages; distributed Internet monitors; traffic monitoring systems; E-mail spam generators and mutators; network firewall logs, blogs and documents ; business and scientific documents. The goal of these systems, ultimately, will be to enable extracting insight and value from this abundant resource (big data) and to support understanding of and solutions to societal or business problems, from improving productivity and efficiency, to creating new economic opportunities in a competitive environment, and to enabling the discovery of new approaches and solutions in business, medicine, science and the humanities. These trends highlight a symbiotic relationship between large-scale data management and software-defined networking. In particular, the main challenges will be the development of new machine learning algorithms that can operate online and at large scale and can give flexible tradeoffs between timeliness, accuracy and cost ; systems infrastructure approaches that allow programmers to easily harness the power of scalable cloud and cluster computing for making sense of data ; crowd sourcing human activity and intelligence to create hybrid human/computer solutions to complex problems for which today's automated data analysis technologies are insufficient. In business, it is generally understood that there is a strong link between effective data management and financial performance. Yet the extraction of economic insight from Big Data remains elusive for most organizations. Heterogeneity, scale, timeliness, complexity and privacy problems with Big Data impede progress at all phases of the pipeline. Many organizations struggle with basic aspects of data management such as cleaning, verifying and reconciling data across the organisation. Additionally, the transformation of complex content into structured format for later analysis remains a daunting challenge. The value of data increases exponentially when its relationship to other intra and inter-organizational data needs to be exploited. Data integration/aggregation is a creator of added-value, but its effective implementation, with its attendant difficulty in semantic convergence, remains an enormous challenge. The ubiquitousness of digital devices provides an opportunity to influence and control both data format and accuracy to facilitate later automatic linkages.

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The most salient nature of software as opposed to hardware is its endurance and adaptability to changes for the better. A software product can be delivered to its end users with minimal marketable features initially. It matures while being used. Therefore it is important to perform dynamic analysis of software requirements to guide its maturation efficiently during its life span. The question accompanying a dynamic analysis of software requirements shoud be, "what can be done?" and "how should we implement it?"
This paper presents a general approach to the dynamic analysis of software requirements with two projects that the author worked in the past as examples. The paper argues as to why dynamic analysis applying to software requirements is twofold to fulfill customer requests tactically and to improve the quality of products strategically.

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The motion estimation techniques used by video compression standards allowan efficient use of transmission and storage resources. In this paper, we propose a new algorithm that reduces the computational load involved, without deteriorating the quality of the reconstructed signal. The new algorithm called AUMHexagonS (Adaptive Unsymmetrical -cross multi -Hexagon-gird Search) is a modification of the UMHexagonS (Unsymmetrical -cross multi -Hexagon-gird Search) that implements a measure that classifies the scene s of a video sequence according to its motion intensity. This motion intensity is used for better operation of the motion estimation steps and for better useof some important H.264/AVC codec parameters.

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This article deals with the generation of the ETL operators (Extract Transform Load) for the purpose of feeding a data warehouse using a relational data source.
This approach enables the designer to define some conditions necessary for the loading.

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Quantum theory introduced us with new methods of keys exchange. The security of those methods has been investigated in the last few years, reaching several different results. In this study one present an overview of quantum key distribution (QKD) protocol and the widespread internet security applications, IPsec and TLS. The research proposed how QKD could be integrated into these security applications. We also note that existing security protocols could be used to authenticate and integrity protect QKD protocol messages, but care must be taken to avoid the use of quantum keys before they exist. Finally we discussed a QKD service interface between the QKD protocol and the security applications. This interface provides a set of common QKD services needed by existing security protocols.

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Grid computing environments have emerged following the demand of scientists to have a very high computing power and storage capacity. It provides scalable infrastructure for storage resource and data files management, which supports several large scale applications. One among the challenges imposed in the use of these environments is the performance problem. To improve performance, scheduling and replicating techniques are used. In this paper we propose an approach to task scheduling combined with data replication decision based on multi criteria principle to improve performance by reducing the response time of tasks and the load of system. This hybrid approach is based on a non-hierarchical model that allows the scalability.

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In this work, we have extended a car-following model on a single-lane (linear model) to simulate the lane-changing behavior by MOBIL (“Minimizing Overall Braking Induced by Lane Changes”) which integrates a politeness factor that controls the possibility to exchange information between vehicles. Each vehicle can be considered as a sender and receiver of information. We investigate inter-vehicle communication which enables vehicle to exchange information generated by microscopic models. There are two basic strategies of information propagation: instantaneous information can be passed backwards to the opposite direction and in front way to the original direction. A current vehicle may transfer kinematic variables to a vehicle driving in the opposite and to original direction. For MOBIL the transmission of information is commanded by a politeness factor. The principal results of simulation concern the transmission of road traffic information at microscopic and macroscopic levels. So, it is necessary to provide the communication technologies which allow an instantaneous information transmission to vehicles and to infrastructure.

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This work investigates image of segmentation of MRI (Magnetic Resonance Imaging) brain by the detector multi scales Canny. The objective is to delineate the outline of a tumor in the MRI images of the brain reaches a frontal meningioma. Accurate and robust segmentation of brain tissue donated by MRI is a very important issue in applications, such as surgery and radiotherapy. The multi-scale Canny edge detector is to use a wavelet transform, which is obtained by orthogonal projection of the image on the affine space of wavelet basis at different scales, we obtain an approximation space and retail space. On the basis thereof, the cards of the modules are calculated and used for the extraction of local maxima. These maxima are decomposition multi scales edges. The result is a set of maxima at different scales membered that we keep only the most significant contours. This method is applied to MRI images of a healthy brain as well as images of a brain with a tumor; comparing the two results the tumor has been localized.

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The peer to peer technologies have recently experienced a large development in the file sharing area and are used strongly by the large public. They are an effective method of sharing resources between people with the same expectations. These systems enjoy a growing reputation, mainly due to the many applications of distributed collaboration and their specific needs of data replication, scalability and high availability. Through this paper, we propose a model to provide users of peer-to-peer systems a good availability of shared data. Our solution is to replicate this data strategically, by defining the data to be replicated, their number, where they will be placed and to remove excess data in order to release space storage if necessary. Our strategy takes into account the data popularity, the volatility and the storage capacity of the different sites.

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