Internet of Things collaborativo: progettazione ed analisi di una piattaforma di aggregazione di dati sensoristici

Internet ha rivoluzionato il modo di comunicare degli individui. Siamo testimoni della nascita e dello sviluppo di un'era caratterizzata dalla disponibilità di informazione libera e accessibile a tutti. Negli ultimi anni grazie alla diffusione di smartphone, tablet e altre tipologie di dispositivi connessi, è cambiato il fulcro dell'innovazione spostandosi dalle persone agli oggetti. E' così che nasce il concetto di Internet of Things, termine usato per descrivere la rete di comunicazione creata tra i diversi dispositivi connessi ad Internet e capaci di interagire in autonomia. Gli ambiti applicativi dell'Internet of Things spaziano dalla domotica alla sanità, dall'environmental monitoring al concetto di smart cities e così via. L'obiettivo principale di tale disciplina è quello di migliorare la vita delle persone grazie a sistemi che siano in grado di interagire senza aver bisogno dell'intervento dell'essere umano. Proprio per la natura eterogenea della disciplina e in relazione ai diversi ambiti applicativi, nell'Internet of Things si può incorrere in problemi derivanti dalla presenza di tecnologie differenti o di modalità eterogenee di memorizzazione dei dati. A questo proposito viene introdotto il concetto di Internet of Things collaborativo, termine che indica l'obiettivo di realizzare applicazioni che possano garantire interoperabilità tra i diversi ecosistemi e tra le diverse fonti da cui l'Internet of Things attinge, sfruttando la presenza di piattaforme di pubblicazione di Open Data. L'obiettivo di questa tesi è stato quello di creare un sistema per l'aggregazione di dati da due piattaforme, ThingSpeak e Sparkfun, con lo scopo di unificarli in un unico database ed estrarre informazioni significative dai dati tramite due tecniche di Data Mining: il Dictionary Learning e l'Affinity Propagation. Vengono illustrate le due metodologie che rientrano rispettivamente tra le tecniche di classificazione e di clustering.

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Sentiment-Topic Modelling in Text Mining

Peer reviewed

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.

Towards Next Generation Vertical Search Engines

As the Web evolves unexpectedly fast, information grows explosively. Useful resources become more and more difficult to find because of their dynamic and unstructured characteristics. A vertical search engine is designed and implemented towards a specific domain. Instead of processing the giant volume of miscellaneous information distributed in the Web, a vertical search engine targets at identifying relevant information in specific domains or topics and eventually provides users with up-to-date information, highly focused insights and actionable knowledge representation. As the mobile device gets more popular, the nature of the search is changing. So, acquiring information on a mobile device poses unique requirements on traditional search engines, which will potentially change every feature they used to have. To summarize, users are strongly expecting search engines that can satisfy their individual information needs, adapt their current situation, and present highly personalized search results. In my research, the next generation vertical search engine means to utilize and enrich existing domain information to close the loop of vertical search engine's system that mutually facilitate knowledge discovering, actionable information extraction, and user interests modeling and recommendation. I investigate three problems in which domain taxonomy plays an important role, including taxonomy generation using a vertical search engine, actionable information extraction based on domain taxonomy, and the use of ensemble taxonomy to catch user's interests. As the fundamental theory, ultra-metric, dendrogram, and hierarchical clustering are intensively discussed. Methods on taxonomy generation using my research on hierarchical clustering are developed. The related vertical search engine techniques are practically used in Disaster Management Domain. Especially, three disaster information management systems are developed and represented as real use cases of my research work.

Inventory of SINOPS project data sets

During the SINOPS project, an optimal state of the art simulation of the marine silicon cycle is attempted employing a biogeochemical ocean general circulation model (BOGCM) through three particular time steps relevant for global (paleo-) climate. In order to tune the model optimally, results of the simulations are compared to a comprehensive data set of 'real' observations. SINOPS' scientific data management ensures that data structure becomes homogeneous throughout the project. Practical work routine comprises systematic progress from data acquisition, through preparation, processing, quality check and archiving, up to the presentation of data to the scientific community. Meta-information and analytical data are mapped by an n-dimensional catalogue in order to itemize the analytical value and to serve as an unambiguous identifier. In practice, data management is carried out by means of the online-accessible information system PANGAEA, which offers a tool set comprising a data warehouse, Graphical Information System (GIS), 2-D plot, cross-section plot, etc. and whose multidimensional data model promotes scientific data mining. Besides scientific and technical aspects, this alliance between scientific project team and data management crew serves to integrate the participants and allows them to gain mutual respect and appreciation.

Modelling and Mining To Manage

In this talk, I will describe various computational modelling and data mining solutions that form the basis of how the office of Deputy Head of Department (Resources) works to serve you. These include lessons I learn about, and from, optimisation issues in resource allocation, uncertainty analysis on league tables, modelling the process of winning external grants, and lessons we learn from student satisfaction surveys, some of which I have attempted to inject into our planning processes.

Context-awareness privacy in data communications

Internet users consume online targeted advertising based on information collected about them and voluntarily share personal information in social networks. Sensor information and data from smart-phones is collected and used by applications, sometimes in unclear ways. As it happens today with smartphones, in the near future sensors will be shipped in all types of connected devices, enabling ubiquitous information gathering from the physical environment, enabling the vision of Ambient Intelligence. The value of gathered data, if not obvious, can be harnessed through data mining techniques and put to use by enabling personalized and tailored services as well as business intelligence practices, fueling the digital economy. However, the ever-expanding information gathering and use undermines the privacy conceptions of the past. Natural social practices of managing privacy in daily relations are overridden by socially-awkward communication tools, service providers struggle with security issues resulting in harmful data leaks, governments use mass surveillance techniques, the incentives of the digital economy threaten consumer privacy, and the advancement of consumergrade data-gathering technology enables new inter-personal abuses. A wide range of fields attempts to address technology-related privacy problems, however they vary immensely in terms of assumptions, scope and approach. Privacy of future use cases is typically handled vertically, instead of building upon previous work that can be re-contextualized, while current privacy problems are typically addressed per type in a more focused way. Because significant effort was required to make sense of the relations and structure of privacy-related work, this thesis attempts to transmit a structured view of it. It is multi-disciplinary - from cryptography to economics, including distributed systems and information theory - and addresses privacy issues of different natures. As existing work is framed and discussed, the contributions to the state-of-theart done in the scope of this thesis are presented. The contributions add to five distinct areas: 1) identity in distributed systems; 2) future context-aware services; 3) event-based context management; 4) low-latency information flow control; 5) high-dimensional dataset anonymity. Finally, having laid out such landscape of the privacy-preserving work, the current and future privacy challenges are discussed, considering not only technical but also socio-economic perspectives.

Discovery driven analysis on semi-structured text data

Discovery Driven Analysis (DDA) is a common feature of OLAP technology to analyze structured data. In essence, DDA helps analysts to discover anomalous data by highlighting 'unexpected' values in the OLAP cube. By giving indications to the analyst on what dimensions to explore, DDA speeds up the process of discovering anomalies and their causes. However, Discovery Driven Analysis (and OLAP in general) is only applicable on structured data, such as records in databases. We propose a system to extend DDA technology to semi-structured text documents, that is, text documents with a few structured data. Our system pipeline consists of two stages: first, the text part of each document is structured around user specified dimensions, using semi-PLSA algorithm; then, we adapt DDA to these fully structured documents, thus enabling DDA on text documents. We present some applications of this system in OLAP analysis and show how scalability issues are solved. Results show that our system can handle reasonable datasets of documents, in real time, without any need for pre-computation.

Analysis and Visualisation of Crowd-sourced Tourism Data

C3S2E '16 Proceedings of the Ninth International C* Conference on Computer Science & Software Engineering

Optimal resource allocation and budgeting in libraries

Libraries since their inception 4000 years ago have been in a process of constant change. Although, changes were in slow motion for centuries, in the last decades, academic libraries have been continuously striving to adapt their services to the ever-changing user needs of students and academic staff. In addition, e-content revolution, technological advances, and ever-shrinking budgets have obliged libraries to efficiently allocate their limited resources among collection and services. Unfortunately, this resource allocation is a complex process due to the diversity of data sources and formats required to be analyzed prior to decision-making, as well as the lack of efficient integration methods. The main purpose of this study is to develop an integrated model that supports libraries in making optimal budgeting and resource allocation decisions among their services and collection by means of a holistic analysis. To this end, a combination of several methodologies and structured approaches is conducted. Firstly, a holistic structure and the required toolset to holistically assess academic libraries are proposed to collect and organize the data from an economic point of view. A four-pronged theoretical framework is used in which the library system and collection are analyzed from the perspective of users and internal stakeholders. The first quadrant corresponds to the internal perspective of the library system that is to analyze the library performance, and costs incurred and resources consumed by library services. The second quadrant evaluates the external perspective of the library system; user’s perception about services quality is judged in this quadrant. The third quadrant analyses the external perspective of the library collection that is to evaluate the impact of the current library collection on its users. Eventually, the fourth quadrant evaluates the internal perspective of the library collection; the usage patterns followed to manipulate the library collection are analyzed. With a complete framework for data collection, these data coming from multiple sources and therefore with different formats, need to be integrated and stored in an adequate scheme for decision support. A data warehousing approach is secondly designed and implemented to integrate, process, and store the holistic-based collected data. Ultimately, strategic data stored in the data warehouse are analyzed and implemented for different purposes including the following: 1) Data visualization and reporting is proposed to allow library managers to publish library indicators in a simple and quick manner by using online reporting tools. 2) Sophisticated data analysis is recommended through the use of data mining tools; three data mining techniques are examined in this research study: regression, clustering and classification. These data mining techniques have been applied to the case study in the following manner: predicting the future investment in library development; finding clusters of users that share common interests and similar profiles, but belong to different faculties; and predicting library factors that affect student academic performance by analyzing possible correlations of library usage and academic performance. 3) Input for optimization models, early experiences of developing an optimal resource allocation model to distribute resources among the different processes of a library system are documented in this study. Specifically, the problem of allocating funds for digital collection among divisions of an academic library is addressed. An optimization model for the problem is defined with the objective of maximizing the usage of the digital collection over-all library divisions subject to a single collection budget. By proposing this holistic approach, the research study contributes to knowledge by providing an integrated solution to assist library managers to make economic decisions based on an “as realistic as possible” perspective of the library situation.

MINING AND VERIFICATION OF TEMPORAL EVENTS WITH APPLICATIONS IN COMPUTER MICRO-ARCHITECTURE RESEARCH

Computer simulation programs are essential tools for scientists and engineers to understand a particular system of interest. As expected, the complexity of the software increases with the depth of the model used. In addition to the exigent demands of software engineering, verification of simulation programs is especially challenging because the models represented are complex and ridden with unknowns that will be discovered by developers in an iterative process. To manage such complexity, advanced verification techniques for continually matching the intended model to the implemented model are necessary. Therefore, the main goal of this research work is to design a useful verification and validation framework that is able to identify model representation errors and is applicable to generic simulators. The framework that was developed and implemented consists of two parts. The first part is First-Order Logic Constraint Specification Language (FOLCSL) that enables users to specify the invariants of a model under consideration. From the first-order logic specification, the FOLCSL translator automatically synthesizes a verification program that reads the event trace generated by a simulator and signals whether all invariants are respected. The second part consists of mining the temporal flow of events using a newly developed representation called State Flow Temporal Analysis Graph (SFTAG). While the first part seeks an assurance of implementation correctness by checking that the model invariants hold, the second part derives an extended model of the implementation and hence enables a deeper understanding of what was implemented. The main application studied in this work is the validation of the timing behavior of micro-architecture simulators. The study includes SFTAGs generated for a wide set of benchmark programs and their analysis using several artificial intelligence algorithms. This work improves the computer architecture research and verification processes as shown by the case studies and experiments that have been conducted.

Exploring Hidden Coherent Feature Groups and Temporal Semantics for Multimedia Big Data Analysis

Thanks to the advanced technologies and social networks that allow the data to be widely shared among the Internet, there is an explosion of pervasive multimedia data, generating high demands of multimedia services and applications in various areas for people to easily access and manage multimedia data. Towards such demands, multimedia big data analysis has become an emerging hot topic in both industry and academia, which ranges from basic infrastructure, management, search, and mining to security, privacy, and applications. Within the scope of this dissertation, a multimedia big data analysis framework is proposed for semantic information management and retrieval with a focus on rare event detection in videos. The proposed framework is able to explore hidden semantic feature groups in multimedia data and incorporate temporal semantics, especially for video event detection. First, a hierarchical semantic data representation is presented to alleviate the semantic gap issue, and the Hidden Coherent Feature Group (HCFG) analysis method is proposed to capture the correlation between features and separate the original feature set into semantic groups, seamlessly integrating multimedia data in multiple modalities. Next, an Importance Factor based Temporal Multiple Correspondence Analysis (i.e., IF-TMCA) approach is presented for effective event detection. Specifically, the HCFG algorithm is integrated with the Hierarchical Information Gain Analysis (HIGA) method to generate the Importance Factor (IF) for producing the initial detection results. Then, the TMCA algorithm is proposed to efficiently incorporate temporal semantics for re-ranking and improving the final performance. At last, a sampling-based ensemble learning mechanism is applied to further accommodate the imbalanced datasets. In addition to the multimedia semantic representation and class imbalance problems, lack of organization is another critical issue for multimedia big data analysis. In this framework, an affinity propagation-based summarization method is also proposed to transform the unorganized data into a better structure with clean and well-organized information. The whole framework has been thoroughly evaluated across multiple domains, such as soccer goal event detection and disaster information management.

Ensemble Stream Model for Data-Cleaning in Sensor Networks

Ensemble Stream Modeling and Data-cleaning are sensor information processing systems have different training and testing methods by which their goals are cross-validated. This research examines a mechanism, which seeks to extract novel patterns by generating ensembles from data. The main goal of label-less stream processing is to process the sensed events to eliminate the noises that are uncorrelated, and choose the most likely model without over fitting thus obtaining higher model confidence. Higher quality streams can be realized by combining many short streams into an ensemble which has the desired quality. The framework for the investigation is an existing data mining tool. First, to accommodate feature extraction such as a bush or natural forest-fire event we make an assumption of the burnt area (BA*), sensed ground truth as our target variable obtained from logs. Even though this is an obvious model choice the results are disappointing. The reasons for this are two: One, the histogram of fire activity is highly skewed. Two, the measured sensor parameters are highly correlated. Since using non descriptive features does not yield good results, we resort to temporal features. By doing so we carefully eliminate the averaging effects; the resulting histogram is more satisfactory and conceptual knowledge is learned from sensor streams. Second is the process of feature induction by cross-validating attributes with single or multi-target variables to minimize training error. We use F-measure score, which combines precision and accuracy to determine the false alarm rate of fire events. The multi-target data-cleaning trees use information purity of the target leaf-nodes to learn higher order features. A sensitive variance measure such as f-test is performed during each node’s split to select the best attribute. Ensemble stream model approach proved to improve when using complicated features with a simpler tree classifier. The ensemble framework for data-cleaning and the enhancements to quantify quality of fitness (30% spatial, 10% temporal, and 90% mobility reduction) of sensor led to the formation of streams for sensor-enabled applications. Which further motivates the novelty of stream quality labeling and its importance in solving vast amounts of real-time mobile streams generated today.