Performance studies of CMS workflows using Big Data technologies

Al Large Hadron Collider (LHC) ogni anno di acquisizione dati vengono raccolti più di 30 petabyte di dati dalle collisioni. Per processare questi dati è necessario produrre un grande volume di eventi simulati attraverso tecniche Monte Carlo. Inoltre l'analisi fisica richiede accesso giornaliero a formati di dati derivati per centinaia di utenti. La Worldwide LHC Computing GRID (WLCG) è una collaborazione interazionale di scienziati e centri di calcolo che ha affrontato le sfide tecnologiche di LHC, rendendone possibile il programma scientifico. Con il prosieguo dell'acquisizione dati e la recente approvazione di progetti ambiziosi come l'High-Luminosity LHC, si raggiungerà presto il limite delle attuali capacità di calcolo. Una delle chiavi per superare queste sfide nel prossimo decennio, anche alla luce delle ristrettezze economiche dalle varie funding agency nazionali, consiste nell'ottimizzare efficientemente l'uso delle risorse di calcolo a disposizione. Il lavoro mira a sviluppare e valutare strumenti per migliorare la comprensione di come vengono monitorati i dati sia di produzione che di analisi in CMS. Per questa ragione il lavoro è comprensivo di due parti. La prima, per quanto riguarda l'analisi distribuita, consiste nello sviluppo di uno strumento che consenta di analizzare velocemente i log file derivanti dalle sottomissioni di job terminati per consentire all'utente, alla sottomissione successiva, di sfruttare meglio le risorse di calcolo. La seconda parte, che riguarda il monitoring di jobs sia di produzione che di analisi, sfrutta tecnologie nel campo dei Big Data per un servizio di monitoring più efficiente e flessibile. Un aspetto degno di nota di tali miglioramenti è la possibilità di evitare un'elevato livello di aggregazione dei dati già in uno stadio iniziale, nonché di raccogliere dati di monitoring con una granularità elevata che tuttavia consenta riprocessamento successivo e aggregazione “on-demand”.

Edge and Big Data technologies for Industry 4.0 to create an integrated pre-sale and after-sale environment

The fourth industrial revolution, also known as Industry 4.0, has rapidly gained traction in businesses across Europe and the world, becoming a central theme in small, medium, and large enterprises alike. This new paradigm shifts the focus from locally-based and barely automated firms to a globally interconnected industrial sector, stimulating economic growth and productivity, and supporting the upskilling and reskilling of employees. However, despite the maturity and scalability of information and cloud technologies, the support systems already present in the machine field are often outdated and lack the necessary security, access control, and advanced communication capabilities. This dissertation proposes architectures and technologies designed to bridge the gap between Operational and Information Technology, in a manner that is non-disruptive, efficient, and scalable. The proposal presents cloud-enabled data-gathering architectures that make use of the newest IT and networking technologies to achieve the desired quality of service and non-functional properties. By harnessing industrial and business data, processes can be optimized even before product sale, while the integrated environment enhances data exchange for post-sale support. The architectures have been tested and have shown encouraging performance results, providing a promising solution for companies looking to embrace Industry 4.0, enhance their operational capabilities, and prepare themselves for the upcoming fifth human-centric revolution.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

A Confluence of Risks: Control and Compliance in the World of Unstructured Data, Big Data and the Cloud

The emergence of powerful new technologies, the existence of large quantities of data, and increasing demands for the extraction of added value from these technologies and data have created a number of significant challenges for those charged with both corporate and information technology management. The possibilities are great, the expectations high, and the risks significant. Organisations seeking to employ cloud technologies and exploit the value of the data to which they have access, be this in the form of "Big Data" available from different external sources or data held within the organisation, in structured or unstructured formats, need to understand the risks involved in such activities. Data owners have responsibilities towards the subjects of the data and must also, frequently, demonstrate that they are in compliance with current standards, laws and regulations. This thesis sets out to explore the nature of the technologies that organisations might utilise, identify the most pertinent constraints and risks, and propose a framework for the management of data from discovery to external hosting that will allow the most significant risks to be managed through the definition, implementation, and performance of appropriate internal control activities.

Big Data: Wrongs and Rights by Andrew Cormack (WAIS Seminar)

Abstract: Big Data has been characterised as a great economic opportunity and a massive threat to privacy. Both may be correct: the same technology can indeed be used in ways that are highly beneficial and those that are ethically intolerable, maybe even simultaneously. Using examples of how Big Data might be used in education - normally referred to as "learning analytics" - the seminar will discuss possible ethical and legal frameworks for Big Data, and how these might guide the development of technologies, processes and policies that can deliver the benefits of Big Data without the nightmares. Speaker Biography: Andrew Cormack is Chief Regulatory Adviser, Jisc Technologies. He joined the company in 1999 as head of the JANET-CERT and EuroCERT incident response teams. In his current role he concentrates on the security, policy and regulatory issues around the network and services that Janet provides to its customer universities and colleges. Previously he worked for Cardiff University running web and email services, and for NERC's Shipboard Computer Group. He has degrees in Mathematics, Humanities and Law.

Big Data or Right Data?

Abstract Big data nowadays is a fashionable topic, independently of what people mean when they use this term. But being big is just a matter of volume, although there is no clear agreement in the size threshold. On the other hand, it is easy to capture large amounts of data using a brute force approach. So the real goal should not be big data but to ask ourselves, for a given problem, what is the right data and how much of it is needed. For some problems this would imply big data, but for the majority of the problems much less data will and is needed. In this talk we explore the trade-offs involved and the main problems that come with big data using the Web as case study: scalability, redundancy, bias, noise, spam, and privacy. Speaker Biography Ricardo Baeza-Yates Ricardo Baeza-Yates is VP of Research for Yahoo Labs leading teams in United States, Europe and Latin America since 2006 and based in Sunnyvale, California, since August 2014. During this time he has lead the labs in Barcelona and Santiago de Chile. Between 2008 and 2012 he also oversaw the Haifa lab. He is also part time Professor at the Dept. of Information and Communication Technologies of the Universitat Pompeu Fabra, in Barcelona, Spain. During 2005 he was an ICREA research professor at the same university. Until 2004 he was Professor and before founder and Director of the Center for Web Research at the Dept. of Computing Science of the University of Chile (in leave of absence until today). He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989. Before he obtained two masters (M.Sc. CS & M.Eng. EE) and the electronics engineer degree from the University of Chile in Santiago. He is co-author of the best-seller Modern Information Retrieval textbook, published in 1999 by Addison-Wesley with a second enlarged edition in 2011, that won the ASIST 2012 Book of the Year award. He is also co-author of the 2nd edition of the Handbook of Algorithms and Data Structures, Addison-Wesley, 1991; and co-editor of Information Retrieval: Algorithms and Data Structures, Prentice-Hall, 1992, among more than 500 other publications. From 2002 to 2004 he was elected to the board of governors of the IEEE Computer Society and in 2012 he was elected for the ACM Council. He has received the Organization of American States award for young researchers in exact sciences (1993), the Graham Medal for innovation in computing given by the University of Waterloo to distinguished ex-alumni (2007), the CLEI Latin American distinction for contributions to CS in the region (2009), and the National Award of the Chilean Association of Engineers (2010), among other distinctions. In 2003 he was the first computer scientist to be elected to the Chilean Academy of Sciences and since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow.

Managing change in complex projects: configuration management, asset information and big data

As we enter an era of ‘big data’, asset information is becoming a deliverable of complex projects. Prior research suggests digital technologies enable rapid, flexible forms of project organizing. This research analyses practices of managing change in Airbus, CERN and Crossrail, through desk-based review, interviews, visits and a cross-case workshop. These organizations deliver complex projects, rely on digital technologies to manage large data-sets; and use configuration management, a systems engineering approach with mid-20th century origins, to establish and maintain integrity. In them, configuration management has become more, rather than less, important. Asset information is structured, with change managed through digital systems, using relatively hierarchical, asynchronous and sequential processes. The paper contributes by uncovering limits to flexibility in complex projects where integrity is important. Challenges of managing change are discussed, considering the evolving nature of configuration management; potential use of analytics on complex projects; and implications for research and practice.

The role of big data in governance: a regulatory and legal perspective of analytics in global financial services

This paper discusses how global financial institutions are using big data analytics within their compliance operations. A lot of previous research has focused on the strategic implications of big data, but not much research has considered how such tools are entwined with regulatory breaches and investigations in financial services. Our work covers two in-depth qualitative case studies, each addressing a distinct type of analytics. The first case focuses on analytics which manage everyday compliance breaches and so are expected by managers. The second case focuses on analytics which facilitate investigation and litigation where serious unexpected breaches may have occurred. In doing so, the study focuses on the micro/data to understand how these tools are influencing operational risks and practices. The paper draws from two bodies of literature, the social studies of information systems and finance to guide our analysis and practitioner recommendations. The cases illustrate how technologies are implicated in multijurisdictional challenges and regulatory conflicts at each end of the operational risk spectrum. We find that compliance analytics are both shaping and reporting regulatory matters yet often firms may have difficulties in recruiting individuals with relevant but diverse skill sets. The cases also underscore the increasing need for financial organizations to adopt robust information governance policies and processes to ease future remediation efforts.

Big data and open data as sustainability tools: A working paper prepared by the Economic Commission for Latin America and the Caribbean

The two main forces affecting economic development are the ongoing technological revolution and the challenge of sustainability. Technological change is altering patterns of production, consumption and behaviour in societies; at the same time, it is becoming increasingly difficult to ensure the sustainability of these new patterns because of the constraints resulting from the negative externalities generated by economic growth and, in many cases, by technical progress itself. Reorienting innovation towards reducing or, if possible, reversing the effects of these externalities could create the conditions for synergies between the two processes. Views on the subject vary widely: while some maintain that these synergies can easily be created if growth follows an environmentally friendly model, summarized in the concept of green growth, others argue that production and consumption patterns are changing too slowly and that any technological fix will come too late. These considerations apply to hard technologies, essentially those used in production. The present document explores the opportunities being opened up by new ones, basically information and communication technologies, in terms of increasing the effectiveness (outcomes) and efficiency (relative costs) of soft technologies that can improve the way environmental issues are handled in business management and in public policy formulation and implementation.

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

Inteligência cibernética e uso de recursos semânticos na detecção de perfis falsos no contexto do Big Data

Pós-graduação em Ciência da Informação - FFC

Algorithms and tools of big data: A bibliographic review

66 p.

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.

Semantics, knowledge and grids on big data

Semantics, knowledge and Grids represent three spaces where people interact, understand, learn and create. Grids represent the advanced cyber-infrastructures and evolution. Big data influence the evolution of semantics, knowledge and Grids. Exploring semantics, knowledge and Grids on big data helps accelerate the shift of scientific paradigm, the fourth industrial revolution, and the transformational innovation of technologies.

Big data analytics and modeling for Human Mobility

The fast development of Information Communication Technologies (ICT) offers new opportunities to realize future smart cities. To understand, manage and forecast the city's behavior, it is necessary the analysis of different kinds of data from the most varied dataset acquisition systems. The aim of this research activity in the framework of Data Science and Complex Systems Physics is to provide stakeholders with new knowledge tools to improve the sustainability of mobility demand in future cities. Under this perspective, the governance of mobility demand generated by large tourist flows is becoming a vital issue for the quality of life in Italian cities' historical centers, which will worsen in the next future due to the continuous globalization process. Another critical theme is sustainable mobility, which aims to reduce private transportation means in the cities and improve multimodal mobility. We analyze the statistical properties of urban mobility of Venice, Rimini, and Bologna by using different datasets provided by companies and local authorities. We develop algorithms and tools for cartography extraction, trips reconstruction, multimodality classification, and mobility simulation. We show the existence of characteristic mobility paths and statistical properties depending on transport means and user's kinds. Finally, we use our results to model and simulate the overall behavior of the cars moving in the Emilia Romagna Region and the pedestrians moving in Venice with software able to replicate in silico the demand for mobility and its dynamic.