Perspectivas e metodologias de pesquisa da Comunicação Social no contexto da internet com o Big Data e da especialização Data Scientist

O trabalho desenvolvido analisa a Comunicação Social no contexto da internet e delineia novas metodologias de estudo para a área na filtragem de significados no âmbito científico dos fluxos de informação das redes sociais, mídias de notícias ou qualquer outro dispositivo que permita armazenamento e acesso a informação estruturada e não estruturada. No intento de uma reflexão sobre os caminhos, que estes fluxos de informação se desenvolvem e principalmente no volume produzido, o projeto dimensiona os campos de significados que tal relação se configura nas teorias e práticas de pesquisa. O objetivo geral deste trabalho é contextualizar a área da Comunicação Social dentro de uma realidade mutável e dinâmica que é o ambiente da internet e fazer paralelos perante as aplicações já sucedidas por outras áreas. Com o método de estudo de caso foram analisados três casos sob duas chaves conceituais a Web Sphere Analysis e a Web Science refletindo os sistemas de informação contrapostos no quesito discursivo e estrutural. Assim se busca observar qual ganho a Comunicação Social tem no modo de visualizar seus objetos de estudo no ambiente das internet por essas perspectivas. O resultado da pesquisa mostra que é um desafio para o pesquisador da Comunicação Social buscar novas aprendizagens, mas a retroalimentação de informação no ambiente colaborativo que a internet apresenta é um caminho fértil para pesquisa, pois a modelagem de dados ganha corpus analítico quando o conjunto de ferramentas promovido e impulsionado pela tecnologia permite isolar conteúdos e possibilita aprofundamento dos significados e suas relações.

A Taxonomy of Big Data for Optimal Predictive Machine Learning and Data Mining

Big data comes in various ways, types, shapes, forms and sizes. Indeed, almost all areas of science, technology, medicine, public health, economics, business, linguistics and social science are bombarded by ever increasing flows of data begging to be analyzed efficiently and effectively. In this paper, we propose a rough idea of a possible taxonomy of big data, along with some of the most commonly used tools for handling each particular category of bigness. The dimensionality p of the input space and the sample size n are usually the main ingredients in the characterization of data bigness. The specific statistical machine learning technique used to handle a particular big data set will depend on which category it falls in within the bigness taxonomy. Large p small n data sets for instance require a different set of tools from the large n small p variety. Among other tools, we discuss Preprocessing, Standardization, Imputation, Projection, Regularization, Penalization, Compression, Reduction, Selection, Kernelization, Hybridization, Parallelization, Aggregation, Randomization, Replication, Sequentialization. Indeed, it is important to emphasize right away that the so-called no free lunch theorem applies here, in the sense that there is no universally superior method that outperforms all other methods on all categories of bigness. It is also important to stress the fact that simplicity in the sense of Ockham’s razor non-plurality principle of parsimony tends to reign supreme when it comes to massive data. We conclude with a comparison of the predictive performance of some of the most commonly used methods on a few data sets.

Progettazione e Sviluppo di un Sistema di Proactive Alerting all'interno di una piattaforma di Big Data Analytics

Il lavoro presentato in questo elaborato tratterà lo sviluppo di un sistema di alerting che consenta di monitorare proattivamente una o più sorgenti dati aziendali, segnalando le eventuali condizioni di irregolarità rilevate; questo verrà incluso all'interno di sistemi già esistenti dedicati all'analisi dei dati e alla pianificazione, ovvero i cosiddetti Decision Support Systems. Un sistema di supporto alle decisioni è in grado di fornire chiare informazioni per tutta la gestione dell'impresa, misurandone le performance e fornendo proiezioni sugli andamenti futuri. Questi sistemi vengono catalogati all'interno del più ampio ambito della Business Intelligence, che sottintende l'insieme di metodologie in grado di trasformare i dati di business in informazioni utili al processo decisionale. L'intero lavoro di tesi è stato svolto durante un periodo di tirocinio svolto presso Iconsulting S.p.A., IT System Integrator bolognese specializzato principalmente nello sviluppo di progetti di Business Intelligence, Enterprise Data Warehouse e Corporate Performance Management. Il software che verrà illustrato in questo elaborato è stato realizzato per essere collocato all'interno di un contesto più ampio, per rispondere ai requisiti di un cliente multinazionale leader nel settore della telefonia mobile e fissa.

Big Data Analysis in Social Networks : Extracting Food Preferences of Vegans from Twitter

Market research is often conducted through conventional methods such as surveys, focus groups and interviews. But the drawbacks of these methods are that they can be costly and timeconsuming. This study develops a new method, based on a combination of standard techniques like sentiment analysis and normalisation, to conduct market research in a manner that is free and quick. The method can be used in many application-areas, but this study focuses mainly on the veganism market to identify vegan food preferences in the form of a profile. Several food words are identified, along with their distribution between positive and negative sentiments in the profile. Surprisingly, non-vegan foods such as cheese, cake, milk, pizza and chicken dominate the profile, indicating that there is a significant market for vegan-suitable alternatives for such foods. Meanwhile, vegan-suitable foods such as coconut, potato, blueberries, kale and tofu also make strong appearances in the profile. Validation is performed by using the method on Volkswagen vehicle data to identify positive and negative sentiment across five car models. Some results were found to be consistent with sales figures and expert reviews, while others were inconsistent. The reliability of the method is therefore questionable, so the results should be used with caution.

La Business Intelligence per il monitoraggio delle vendite: il caso Ducati Motor Holding

“La Business Intelligence per il monitoraggio delle vendite: il caso Ducati Motor Holding”. L’obiettivo di questa tesi è quello di illustrare cos’è la Business Intelligence e di mostrare i cambiamenti verificatisi in Ducati Motor Holding, in seguito alla sua adozione, in termini di realizzazione di report e dashboard per il monitoraggio delle vendite. L’elaborato inizia con una panoramica generale sulla storia e gli utilizzi della Business Intelligence nella quale vengono toccati i principali fondamenti teorici: Data Warehouse, data mining, analisi what-if, rappresentazione multidimensionale dei dati, costruzione del team di BI eccetera. Si proseguirà mediante un focus sui Big Data convogliando l’attenzione sul loro utilizzo e utilità nel settore dell’automotive (inteso nella sua accezione più generica e cioè non solo come mercato delle auto, ma anche delle moto), portando in questo modo ad un naturale collegamento con la realtà Ducati. Si apre così una breve overview sull’azienda descrivendone la storia, la struttura commerciale attraverso la quale vengono gestite le vendite e la gamma dei prodotti. Dal quarto capitolo si entra nel vivo dell’argomento: la Business Intelligence in Ducati. Si inizia descrivendo le fasi che hanno fino ad ora caratterizzato il progetto di Business Analytics (il cui obiettivo è per l'appunto introdurre la BI i azienda) per poi concentrarsi, a livello prima teorico e poi pratico, sul reporting sales e cioè sulla reportistica basata sul monitoraggio delle vendite.

Consequences of big data on the individual: Interview

The speed with which data has moved from being scarce, expensive and valuable, thus justifying detailed and careful verification and analysis to a situation where the streams of detailed data are almost too large to handle has caused a series of shifts to occur. Legal systems already have severe problems keeping up with, or even in touch with, the rate at which unexpected outcomes flow from information technology. The capacity to harness massive quantities of existing data has driven Big Data applications until recently. Now the data flows in real time are rising swiftly, become more invasive and offer monitoring potential that is eagerly sought by commerce and government alike. The ambiguities as to who own this often quite remarkably intrusive personal data need to be resolved – and rapidly - but are likely to encounter rising resistance from industrial and commercial bodies who see this data flow as ‘theirs’. There have been many changes in ICT that has led to stresses in the resolution of the conflicts between IP exploiters and their customers, but this one is of a different scale due to the wide potential for individual customisation of pricing, identification and the rising commercial value of integrated streams of diverse personal data. A new reconciliation between the parties involved is needed. New business models, and a shift in the current confusions over who owns what data into alignments that are in better accord with the community expectations. After all they are the customers, and the emergence of information monopolies needs to be balanced by appropriate consumer/subject rights. This will be a difficult discussion, but one that is needed to realise the great benefits to all that are clearly available if these issues can be positively resolved. The customers need to make these data flow contestable in some form. These Big data flows are only going to grow and become ever more instructive. A better balance is necessary, For the first time these changes are directly affecting governance of democracies, as the very effective micro targeting tools deployed in recent elections have shown. Yet the data gathered is not available to the subjects. This is not a survivable social model. The Private Data Commons needs our help. Businesses and governments exploit big data without regard for issues of legality, data quality, disparate data meanings, and process quality. This often results in poor decisions, with individuals bearing the greatest risk. The threats harbored by big data extend far beyond the individual, however, and call for new legal structures, business processes, and concepts such as a Private Data Commons. This Web extra is the audio part of a video in which author Marcus Wigan expands on his article "Big Data's Big Unintended Consequences" and discusses how businesses and governments exploit big data without regard for issues of legality, data quality, disparate data meanings, and process quality. This often results in poor decisions, with individuals bearing the greatest risk. The threats harbored by big data extend far beyond the individual, however, and call for new legal structures, business processes, and concepts such as a Private Data Commons.

Il passaggio dalla Business Intelligence all'Advanced Analytics: individuazione e analisi di un modello in merito all'influenza del lobbying nel sistema economico

I Big Data stanno guidando una rivoluzione globale. In tutti i settori, pubblici o privati, e le industrie quali Vendita al dettaglio, Sanità, Media e Trasporti, i Big Data stanno influenzando la vita di miliardi di persone. L’impatto dei Big Data è sostanziale, ma così discreto da passare inosservato alla maggior parte delle persone. Le applicazioni di Business Intelligence e Advanced Analytics vogliono studiare e trarre informazioni dai Big Data. Si studia il passaggio dalla prima alla seconda, mettendo in evidenza aspetti simili e differenze.

Usability of small crisis data sets in the absence of big data

Big data is big news in almost every sector including crisis communication. However, not everyone has access to big data and even if we have access to big data, we often do not have necessary tools to analyze and cross reference such a large data set. Therefore this paper looks at patterns in small data sets that we have ability to collect with our current tools to understand if we can find actionable information from what we already have. We have analyzed 164390 tweets collected during 2011 earthquake to find out what type of location specific information people mention in their tweet and when do they talk about that. Based on our analysis we find that even a small data set that has far less data than a big data set can be useful to find priority disaster specific areas quickly.

Secured e-health data retrieval in DaaS and Big Data

Big Data is a rising IT trend similar to cloud computing, social networking or ubiquitous computing. Big Data can offer beneficial scenarios in the e-health arena. However, one of the scenarios can be that Big Data needs to be kept secured for a long period of time in order to gain its benefits such as finding cures for infectious diseases and protecting patient privacy. From this connection, it is beneficial to analyse Big Data to make meaningful information while the data is stored securely. Therefore, the analysis of various database encryption techniques is essential. In this study, we simulated 3 types of technical environments, namely, Plain-text, Microsoft Built-in Encryption, and custom Advanced Encryption Standard, using Bucket Index in Data-as-a-Service. The results showed that custom AES-DaaS has a faster range query response time than MS built-in encryption. Furthermore, while carrying out the scalability test, we acknowledged that there are performance thresholds depending on physical IT resources. Therefore, for the purpose of efficient Big Data management in eHealth it is noteworthy to examine their scalability limits as well even if it is under a cloud computing environment. In addition, when designing an e-health database, both patient privacy and system performance needs to be dealt as top priorities.

Augmenting analytical CRM strategies with social BI

Large communities built around social media on the Internet offer an opportunity to augment analytical customer relationship management (CRM) strategies. The purpose of this paper is to provide direction to advance the conceptual design of business intelligence (BI) systems for implementing CRM strategies. After introducing social CRM and social BI as emerging fields of research, the authors match CRM strategies with a re-engineered conceptual data model of Facebook in order to illustrate the strategic value of these data. Subsequently, the authors design a multi-dimensional data model for social BI and demonstrate its applicability by designing management reports in a retail scenario. Building on the service blueprinting framework, the authors propose a structured research agenda for the emerging field of social BI.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Health big data analytics : current perspectives, challenges and potential solutions

Modern health information systems can generate several exabytes of patient data, the so called "Health Big Data", per year. Many health managers and experts believe that with the data, it is possible to easily discover useful knowledge to improve health policies, increase patient safety and eliminate redundancies and unnecessary costs. The objective of this paper is to discuss the characteristics of Health Big Data as well as the challenges and solutions for health Big Data Analytics (BDA) – the process of extracting knowledge from sets of Health Big Data – and to design and evaluate a pipelined framework for use as a guideline/reference in health BDA.

The big data lottery

Big data is certainly the buzz term in executive networking circles at the moment. Heralded by management consultancies and research organisations alike as the next big thing in business efficiency, it is shooting up the Gartner hype cycle to the giddy heights of the peak of inflated expectations before it tumbles down in to the trough of disillusionment

The unfinished revolution : what is missing from the E&P industry’s move to “big data”

One cannot help but be impressed by the inroads that digital oilfield technologies have made into the exploration and production (E&P) industry in the past decade. Today’s production systems can be monitored by “smart” sensors that allow engineers to observe almost any aspect of performance in real time. Our understanding of how reservoirs are behaving has improved considerably since the dawn of this revolution, and the industry has been able to move away from point answers to more holistic “big picture” integrated solutions. Indeed, the industry has already reaped the rewards of many of these kinds of investments. Many billions of dollars of value have been delivered by this heightened awareness of what is going on within our assets and the world around them (Van Den Berg et al. 2010).

Metaphors of big data

Metaphors are a common instrument of human cognition, activated when seeking to make sense of novel and abstract phenomena. In this article we assess some of the values and assumptions encoded in the framing of the term big data, drawing on the framework of conceptual metaphor. We first discuss the terms data and big data and the meanings historically attached to them by different usage communities and then proceed with a discourse analysis of Internet news items about big data. We conclude by characterizing two recurrent framings of the concept: as a natural force to be controlled and as a resource to be consumed.