Open Season for Data Fishing on the Web: The Challenges of the US PRISM Programme for the EU. CEPS Policy Brief No. 293, 18 June 2013

The revelation of the top-secret US intelligence-led PRISM Programme has triggered wide-ranging debates across Europe. Press reports have shed new light on the electronic surveillance ‘fishing expeditions’ of the US National Security Agency and the FBI into the world’s largest electronic communications companies. This Policy Brief by a team of legal specialists and political scientists addresses the main controversies raised by the PRISM affair and the policy challenges that it poses for the EU. Two main arguments are presented: First, the leaks over the PRISM programme have undermined the trust that EU citizens have in their governments and the European institutions to safeguard and protect their privacy; and second, the PRISM affair raises questions regarding the capacity of EU institutions to draw lessons from the past and to protect the data of its citizens and residents in the context of transatlantic relations. The Policy Brief puts forward a set of policy recommendations for the EU to follow and implement a robust data protection strategy in response to the affair.

Data capturing: studio di fattibilità per l’acquisizione automatizzata delle informazioni lungo la catena produttiva

Il processo di Data Entry manuale non solo è oneroso dal punto di vista temporale ed economico, lo è ancor di più poiché rappresenta una fonte di errore: per questi motivi, l’acquisizione automatizzata delle informazioni lungo la catena produttiva è un obiettivo fortemente desiderato dal Gruppo per migliorare i propri business. Le tecnologie analizzate, ormai diffuse e standardizzate in ampia scala come barcode, etichette logistiche, terminali in radiofrequenza, possono apportare grandi benefici ai processi aziendali, ancor più integrandole su misura agli ERP aziendali, permettendo una registrazione rapida e corretta delle informazioni e la diffusione immediata delle stesse all’intera organizzazione. L’analisi dei processi e dei flussi hanno evidenziato le criticità e permesso di capire dove e quando intervenire con una progettazione che risultasse quanto più la best suite possibile. Il lancio dei fabbisogni, l’entrata, la mappatura e la movimentazione merci in Magazzino, lo stato di produzione, lo scarico componenti ed il carico di produzione in Confezionamento e Semilavorazione, l’istituzione di un magazzino di interscambio Dogana, un flusso di tracciabilità preciso e rapido, sono tutti eventi che modificheranno i processi aziendali, snellendoli e svincolando risorse che potranno essere reinvestite in operatività a valore aggiunto superiore. I risultati potenzialmente ottenibili, comprovati anche dalle esperienze esterne di fornitori e consulenza, hanno generato le condizioni necessarie ad un rapido studio e start dei lavori: il Gruppo è entusiasta ed impaziente di portare a termine quanto prima il progetto e di andare a regime con la nuova modalità operativa, snellita ed ottimizzata.

Fuzzy logic in web data mining for website assessment

This paper proposes a novel application of fuzzy logic to web data mining for two basic problems of a website: popularity and satisfaction. Popularity means that people will visit the website while satisfaction refers to the usefulness of the site. We will illustrate that the popularity of a website is a fuzzy logic problem. It is an important characteristic of a website in order to survive in Internet commerce. The satisfaction of a website is also a fuzzy logic problem that represents the degree of success in the application of information technology to the business. We propose a framework of fuzzy logic for the representation of these two problems based on web data mining techniques to fuzzify the attributes of a website.

Optimizing the data intensive mediator-based Web services composition

Effectively using heterogeneous, distributed information has attracted much research in recent years. Current web services technologies have been used successfully in some non data intensive distributed prototype systems. However, most of them can not work well in data intensive environment. This paper provides an infrastructure layer in data intensive environment for the effectively providing spatial information services by using the web services over the Internet. We extensively investigate and analyze the overhead of web services in data intensive environment, and propose some new optimization techniques which can greatly increase the system’s efficiency. Our experiments show that these techniques are suitable to data intensive environment. Finally, we present the requirement of these techniques for the information of web services over the Internet.

Efficient Data Management with Applications to IoT

The Internet of Things (IoT) consists of a worldwide “network of networks,” composed by billions of interconnected heterogeneous devices denoted as things or “Smart Objects” (SOs). Significant research efforts have been dedicated to port the experience gained in the design of the Internet to the IoT, with the goal of maximizing interoperability, using the Internet Protocol (IP) and designing specific protocols like the Constrained Application Protocol (CoAP), which have been widely accepted as drivers for the effective evolution of the IoT. This first wave of standardization can be considered successfully concluded and we can assume that communication with and between SOs is no longer an issue. At this time, to favor the widespread adoption of the IoT, it is crucial to provide mechanisms that facilitate IoT data management and the development of services enabling a real interaction with things. Several reference IoT scenarios have real-time or predictable latency requirements, dealing with billions of device collecting and sending an enormous quantity of data. These features create a new need for architectures specifically designed to handle this scenario, hear denoted as “Big Stream”. In this thesis a new Big Stream Listener-based Graph architecture is proposed. Another important step, is to build more applications around the Web model, bringing about the Web of Things (WoT). As several IoT testbeds have been focused on evaluating lower-layer communication aspects, this thesis proposes a new WoT Testbed aiming at allowing developers to work with a high level of abstraction, without worrying about low-level details. Finally, an innovative SOs-driven User Interface (UI) generation paradigm for mobile applications in heterogeneous IoT networks is proposed, to simplify interactions between users and things.

Implementation challenges of application of radio frequency identification (RFID) in warehouse

Warehouse is an essential component in the supply chain, linking the chain partners and providing them with functions of product storage, inbound and outbound operations along with value-added processes. Allocation of warehouse resources should be efficient and effective to achieve optimum productivity and reduce operational costs. Radio frequency identification (RFID) is a technology capable of providing real-time information about supply chain operations. It has been used by warehousing and logistic enterprises to achieve reduced shrinkage, improved material handling and tracking as well as increased accuracy of data collection. However, both academics and practitioners express concerns about challenges to RFID adoption in the supply chain. This paper provides a comprehensive analysis of the problems encountered in RFID implementation at warehouses, discussing the theoretical and practical adoption barriers and causes of not achieving full potential of the technology. Lack of foreseeable return on investment (ROI) and high costs are the most commonly reported obstacles. Variety of standards and radio wave frequencies are identified as source of concern for decision makers. Inaccurate performance of the RFID within the warehouse environment is examined. Description of integration challenges between warehouse management system and RFID technology is given. The paper discusses the existing solutions to technological, investment and performance RFID adoption barriers. Factors to consider when implementing the RFID technology are given to help alleviate implementation problems. By illustrating the challenges of RFID in the warehouse environment and discussing possible solutions the paper aims to help both academics and practitioners to focus on key areas constituting an obstacle to the technology growth. As more studies will address these challenges, the realisation of RFID benefits for warehouses and supply chain will become a reality.

Communities and emerging semantics in semantic link network:discovery and learning

The World Wide Web provides plentiful contents for Web-based learning, but its hyperlink-based architecture connects Web resources for browsing freely rather than for effective learning. To support effective learning, an e-learning system should be able to discover and make use of the semantic communities and the emerging semantic relations in a dynamic complex network of learning resources. Previous graph-based community discovery approaches are limited in ability to discover semantic communities. This paper first suggests the Semantic Link Network (SLN), a loosely coupled semantic data model that can semantically link resources and derive out implicit semantic links according to a set of relational reasoning rules. By studying the intrinsic relationship between semantic communities and the semantic space of SLN, approaches to discovering reasoning-constraint, rule-constraint, and classification-constraint semantic communities are proposed. Further, the approaches, principles, and strategies for discovering emerging semantics in dynamic SLNs are studied. The basic laws of the semantic link network motion are revealed for the first time. An e-learning environment incorporating the proposed approaches, principles, and strategies to support effective discovery and learning is suggested.

Relation discovery from web data for competency management

In current organizations, valuable enterprise knowledge is often buried under rapidly expanding huge amount of unstructured information in the form of web pages, blogs, and other forms of human text communications. We present a novel unsupervised machine learning method called CORDER (COmmunity Relation Discovery by named Entity Recognition) to turn these unstructured data into structured information for knowledge management in these organizations. CORDER exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments in an expert evaluation, a quantitative benchmarking, and an application of CORDER in a social networking tool called BuddyFinder.

Mining web data for competency management

We present CORDER (COmmunity Relation Discovery by named Entity Recognition) an un-supervised machine learning algorithm that exploits named entity recognition and co-occurrence data to associate individuals in an organization with their expertise and associates. We discuss the problems associated with evaluating unsupervised learners and report our initial evaluation experiments.

Site-restricted web searches for data collection in regional dialectology

This article presents a new method for data collection in regional dialectology based on site-restricted web searches. The method measures the usage and determines the distribution of lexical variants across a region of interest using common web search engines, such as Google or Bing. The method involves estimating the proportions of the variants of a lexical alternation variable over a series of cities by counting the number of webpages that contain the variants on newspaper websites originating from these cities through site-restricted web searches. The method is evaluated by mapping the 26 variants of 10 lexical variables with known distributions in American English. In almost all cases, the maps based on site-restricted web searches align closely with traditional dialect maps based on data gathered through questionnaires, demonstrating the accuracy of this method for the observation of regional linguistic variation. However, unlike collecting dialect data using traditional methods, which is a relatively slow process, the use of site-restricted web searches allows for dialect data to be collected from across a region as large as the United States in a matter of days.