Analisi sulla crescita e sulle funzioni dei Linked Open Data - LODStories

L'Open Data, letteralmente ���dati aperti���, �� la corrente di pensiero (e il relativo ���movimento���) che cerca di rispondere all'esigenza di poter disporre di dati legalmente ���aperti���, ovvero liberamente re-usabili da parte del fruitore, per qualsiasi scopo. L���obiettivo dell���Open Data pu�� essere raggiunto per legge, come negli USA dove l���informazione generata dal settore pubblico federale �� in pubblico dominio, oppure per scelta dei detentori dei diritti, tramite opportune licenze. Per motivare la necessit�� di avere dei dati in formato aperto, possiamo usare una comparazione del tipo: l'Open Data sta al Linked Data, come la rete Internet sta al Web. L'Open Data, quindi, �� l���infrastruttura (o la ���piattaforma���) di cui il Linked Data ha bisogno per poter creare la rete di inferenze tra i vari dati sparsi nel Web. Il Linked Data, in altre parole, �� una tecnologia ormai abbastanza matura e con grandi potenzialit��, ma ha bisogno di grandi masse di dati tra loro collegati, ossia ���linkati���, per diventare concretamente utile. Questo, in parte, �� gi�� stato ottenuto ed �� in corso di miglioramento, grazie a progetti come DBpedia o FreeBase. In parallelo ai contributi delle community online, un altro tassello importante ��� una sorta di ���bulk upload��� molto prezioso ��� potrebbe essere dato dalla disponibilit�� di grosse masse di dati pubblici, idealmente anche gi�� linkati dalle istituzioni stesse o comunque messi a disposizione in modo strutturato ��� che aiutino a raggiungere una ���massa��� di Linked Data. A partire dal substrato, rappresentato dalla disponibilit�� di fatto dei dati e dalla loro piena riutilizzabilit�� (in modo legale), il Linked Data pu�� offrire una potente rappresentazione degli stessi, in termini di relazioni (collegamenti): in questo senso, Linked Data ed Open Data convergono e raggiungono la loro piena realizzazione nell���approccio Linked Open Data. L���obiettivo di questa tesi �� quello di approfondire ed esporre le basi sul funzionamento dei Linked Open Data e gli ambiti in cui vengono utilizzati.

Combining data driven programming with component based software development:with applications in geovisualisation and dynamic data driven application systems

Software development methodologies are becoming increasingly abstract, progressing from low level assembly and implementation languages such as C and Ada, to component based approaches that can be used to assemble applications using technologies such as JavaBeans and the .NET framework. Meanwhile, model driven approaches emphasise the role of higher level models and notations, and embody a process of automatically deriving lower level representations and concrete software implementations. The relationship between data and software is also evolving. Modern data formats are becoming increasingly standardised, open and empowered in order to support a growing need to share data in both academia and industry. Many contemporary data formats, most notably those based on XML, are self-describing, able to specify valid data structure and content, and can also describe data manipulations and transformations. Furthermore, while applications of the past have made extensive use of data, the runtime behaviour of future applications may be driven by data, as demonstrated by the field of dynamic data driven application systems. The combination of empowered data formats and high level software development methodologies forms the basis of modern game development technologies, which drive software capabilities and runtime behaviour using empowered data formats describing game content. While low level libraries provide optimised runtime execution, content data is used to drive a wide variety of interactive and immersive experiences. This thesis describes the Fluid project, which combines component based software development and game development technologies in order to define novel component technologies for the description of data driven component based applications. The thesis makes explicit contributions to the fields of component based software development and visualisation of spatiotemporal scenes, and also describes potential implications for game development technologies. The thesis also proposes a number of developments in dynamic data driven application systems in order to further empower the role of data in this field.

Constructing a traditional Chinese medicine data warehouse application

The explosive growth in the development of Traditional Chinese Medicine (TCM) has resulted in the continued increase in clinical and research data. The lack of standardised terminology, flaws in data quality planning and management of TCM informatics are preventing clinical decision-making, drug discovery and education. This paper argues that the introduction of data warehousing technologies to enhance the effectiveness and durability in TCM is paramount. To showcase the role of data warehousing in the improvement of TCM, this paper presents a practical model for data warehousing with detailed explanation, which is based on the structured electronic records, for TCM clinical researches and medical knowledge discovery.

Reputation model based on rating data and application in recommender systems

This thesis introduced two novel reputation models to generate accurate item reputation scores using ratings data and the statistics of the dataset. It also presented an innovative method that incorporates reputation awareness in recommender systems by employing voting system methods to produce more accurate top-N item recommendations. Additionally, this thesis introduced a personalisation method for generating reputation scores based on users' interests, where a single item can have different reputation scores for different users. The personalised reputation scores are then used in the proposed reputation-aware recommender systems to enhance the recommendation quality.

Measuring Fishing Capacity and Utilization with Commonly Available Data: An Application to Alaska Fisheries

Due to a lack of data on vessel costs, earnings, and input use, many of the capacity assessment models developed in the economics literature cannot be applied in U.S. fisheries. This incongruity between available data and model requirements underscores the need for developing applicable methodologies. This paper presents a means of assessing fishing capacity and utilization (for both vessels and fish stocks) with commonly available data, while avoiding some of the shortcomings associated with competing ���frontier��� approaches (such as data envelopment analys

Energy efficient scheduling for cluster-tree wireless sensor networks with time-bounded data flows: application to IEEE 802.15.4/ZigBee

Cluster scheduling and collision avoidance are crucial issues in large-scale cluster-tree Wireless Sensor Networks (WSNs). The paper presents a methodology that provides a Time Division Cluster Scheduling (TDCS) mechanism based on the cyclic extension of RCPS/TC (Resource Constrained Project Scheduling with Temporal Constraints) problem for a cluster-tree WSN, assuming bounded communication errors. The objective is to meet all end-to-end deadlines of a predefined set of time-bounded data flows while minimizing the energy consumption of the nodes by setting the TDCS period as long as possible. Sinceeach cluster is active only once during the period, the end-to-end delay of a given flow may span over several periods when there are the flows with opposite direction. The scheduling tool enables system designers to efficiently configure all required parameters of the IEEE 802.15.4/ZigBee beaconenabled cluster-tree WSNs in the network design time. The performance evaluation of thescheduling tool shows that the problems with dozens of nodes can be solved while using optimal solvers.

Compositional ideas in the bayesian analysis of categorical data with application to dose finding clinical trials

Compositional random vectors are fundamental tools in the Bayesian analysis of categorical data. Many of the issues that are discussed with reference to the statistical analysis of compositional data have a natural counterpart in the construction of a Bayesian statistical model for categorical data. This note builds on the idea of cross-fertilization of the two areas recommended by Aitchison (1986) in his seminal book on compositional data. Particular emphasis is put on the problem of what parameterization to use

Analytical potentials for triatomic molecules from spectroscopic data V. Application to HOX (X=F, Cl, Br, I)

Analytical potential energy functions are reported for HOX (X=F, Cl, Br, I). The surface for HOF predicts two metastable minima as well as the equilibrium configuration. These correspond to HFO (bent) and OHF (linear). Ab initio calculations performed for the HOF surface confirm these predictions. Comparisons are drawn between the two sets of results, and a vibrational analysis is undertaken for the hydrogen bonded OHF species. For HOCl, one further minimum is predicted, corresponding to HClO (bent), the parameters for which compare favourably with those reported from ab initio studies. In contrast, only the equilibrium configurations are predicted to be stable for HOBr and HOI.

The development of a data-driven application benchmarking approach to performance modelling

Performance modelling is a useful tool in the lifeycle of high performance scientific software, such as weather and climate models, especially as a means of ensuring efficient use of available computing resources. In particular, sufficiently accurate performance prediction could reduce the effort and experimental computer time required when porting and optimising a climate model to a new machine. In this paper, traditional techniques are used to predict the computation time of a simple shallow water model which is illustrative of the computation (and communication) involved in climate models. These models are compared with real execution data gathered on AMD Opteron-based systems, including several phases of the U.K. academic community HPC resource, HECToR. Some success is had in relating source code to achieved performance for the K10 series of Opterons, but the method is found to be inadequate for the next-generation Interlagos processor. The experience leads to the investigation of a data-driven application benchmarking approach to performance modelling. Results for an early version of the approach are presented using the shallow model as an example.

Geoadditive models for regional count data: an application to industrial location

We propose a geoadditive negative binomial model (Geo-NB-GAM) for regional count data that allows us to address simultaneously some important methodological issues, such as spatial clustering, nonlinearities, and overdispersion. This model is applied to the study of location determinants of inward greenfield investments that occurred during 2003���2007 in 249 European regions. After presenting the data set and showing the presence of overdispersion and spatial clustering, we review the theoretical framework that motivates the choice of the location determinants included in the empirical model, and we highlight some reasons why the relationship between some of the covariates and the dependent variable might be nonlinear. The subsequent section first describes the solutions proposed by previous literature to tackle spatial clustering, nonlinearities, and overdispersion, and then presents the Geo-NB-GAM. The empirical analysis shows the good performance of Geo-NB-GAM. Notably, the inclusion of a geoadditive component (a smooth spatial trend surface) permits us to control for spatial unobserved heterogeneity that induces spatial clustering. Allowing for nonlinearities reveals, in keeping with theoretical predictions, that the positive effect of agglomeration economies fades as the density of economic activities reaches some threshold value. However, no matter how dense the economic activity becomes, our results suggest that congestion costs never overcome positive agglomeration externalities.

A data mining application of the incidence semirings

This paper is devoted to a combinatorial problem for incidence semirings, which can be viewed as sets of polynomials over graphs, where the edges are the unknowns and the coefficients are taken from a semiring. The construction of incidence rings is very well known and has many useful applications. The present article is devoted to a novel application of the more general incidence semirings. Recent research on data mining has motivated the investigation of the sets of centroids that have largest weights in semiring constructions. These sets are valuable for the design of centroid-based classification systems, or classifiers, as well as for the design of multiple classifiers combining several individual classifiers. Our article gives a complete description of all sets of centroids with the largest weight in incidence semirings.