Big Data Visualisation

Dalam makalah ini dipaparkan suatu kajian mengenai peran big data dalam riset-riset teknologi informasi terkini, pengaruh, serta potensinya dalam perkembangan ilmu komputer. Secara khusus dipaparkan mengenai peran temu balik informasi sebagai salah satu bidang riset dalam ilmu komputer untuk pengelolaan big data. Dari beberapa eksperimen yang telah dilakukan, dengan menggunakan pendekatan dari bidang ilmu temu balik informasi tersebut, dapat disampaikan empat isu besar yang menyertai perkembangan dan pengelolaan big data, yaitu: 1). infrastruktur jaringan komputer; 2). normalisasi (penyamaan) persepsi terhadap data (data science); 3). big data sebagai fondasi untuk collaborative intelligence; dan 4). perlunya kesaradaran akan nilai informasi yang mengiringi citra diri seseorang, sebuah organisasi ataupun institusi. Keywords — big data, temu balik informasi, penyaringan data, data science. I. KONSEP BIG DATA Beranjak dari tema umum yang ditawarkan dalam SeTISI 2015, yaitu: 'Peran...

In our article, we investigate the affective economy of national-populist image circulation on Facebook. This is highly relevant, since social media has been an essential area for the spread of national-populist ideology. In our research, we analyse image circulation as affective practice, combining qualitative and quantitative methods. We use computational data analysis methods to examine visual big data: image fingerprints and reverse image search engines to track down the routes of thousands of circulated images as well as make discourse-historical analysis on the images that have gained most attention among supporters. Our research demonstrates that these existing tools allow social science research to make theory-solid approaches to understand the role of image circulation in creating and sustaining national and transnational networks on social media, and show how national-populist thinking is spread through images that catalyse and mobilise affects – fear, anger and resentment – thus creating an effective affective economy.

Until recently, knowledge-intensive work activities have predominantly taken place in office buildings as a specialized form of economic infrastructure. New digital technologies together with an economic and organizational transition from closed firms to open platforms has changed the pattern of work within the modern metropolis. The office building is no longer the sole workplace typology and work activity has intensified in other urban locations. The questions then are: "How might smart cities reinterpret workplace culture at the urban scale outside the framework of office buildings typology?" and "Which tools and methodologies can be used to make digital workplace culture visible at the urban scale?" In order to answer these questions, workplaces are observed not as private architectural spaces but as compositions of "subjective urban experiences". A Twitter data analysis provides evidence of workplace spatial culture within the innovative global cities of Amsterdam, London and Paris, interpreted as behavior settings. This analysis shows that office pattern locations are generally distributed independently to knowledge intensive business services and workplace demand, as expressed through social media analyses. In addition to office buildings, transit hubs, urban amenities and new digital services play a key role in reframing workplace location. Moving beyond generic visions for digital work in outer spaces, big data therefore provides specific insights and incentives for considering workplace design at the urban scale.

3rd International Conference on Big Data and Machine Learning (BDML 2022) will act as a major forum for the presentation of innovative ideas, approaches, developments, and research projects in the areas of Big Data and Machine Learning. It will also serve to facilitate the exchange of information between researchers and industry professionals to discuss the latest issues and advancement in the area of Big Data and Machine Learning.

Avant le dernier « sprint final » pour remporter la prochaine présidentielle qui se gagnera en partie sur le Web, un grand vainqueur s'impose d'ores et déjà au coeur de la campagne électorale : l'usage immodéré des plateformes de Data Intelligence américaines par toutes les écuries politiques en lice.

در عصر فناوری ‌اطلاعات و ارتباطات، نگرش مصرف‌کنندگان به ادراک از خلاقیت پیامک‌های تبلیغاتی، یکی از موضوعات مطرح در حوزه بازاریابی است و هنوز شناخت چندانی درباره آن وجود ندارد. هدف این پژوهش، بررسی تأثیر ادراک از خلاقیت پیامک‌های تبلیغاتی بر نگرش و واکنش مصرف‌کنندگان به پیامک‌های تبلیغاتی ارسالی به تلفن همراه آنهاست. جامعه آماری این تحقیق کاربران تلفن همراه در شهر تهران و حجم نمونه ۳۸۵ نفر است که به‌صورت نمونه‌گیری دردسترس انتخاب شدند. تحقیق حاضر از نظر هدف، کاربردی و از نظر نحوه گردآوری‌ داده‌ها توصیفی- پیمایشی است. مدل مورد مطالعه با استفاده از مدل‌سازی معادلات ساختاری و رویکرد کمترین مربعات جزئی آزمون شد. نتایج بیانگر آن است که ادراک از خلاقیت پیامک تبلیغاتی بر نگرش و واکنش‌رفتاری مصرف‌کنندگان به پیامک‌های تبلیغاتی ارسالی به تلفن همراه آنان تأثیر مثبت معناداری دارد، درحالی‌که ادراک از خلاقیت پیامک تبلیغاتی به‌صورت غیرمستقیم و از طریق متغیر میانجی نگرش نیز بر واکنش ‌رفتاری مصرف‌کنندگان تأثیر می‌گذارد.

The analytics of data holds an important function by the reduction of the size and complicated nature of data in data mining. Data Visualization is a major method which aids big data to get an absolute data perspective and as well the discovery of data values. Since the visualization techniques are so enormous, it can at times be difficult to know what appropriate technique to be used to visualize data. The purpose of representing data visually is to basically give an interpretation to what is insight without difficulties. At different point in time, the visualization techniques are being used to carry out different task which as well communicates different level of understanding. In this paper, we examined various data mining visualization techniques and how they can be well understood and utilized and then we made discussed our contributions in making research about the adequacy and inadequacy of data visualization technique in handling big data.

El big data nos brinda un acceso inédito a conocer aquello que el electorado cree, siente, le interesa, le preocupa, lo moviliza para poder diseñar campañas que lo interpelen. Este artículo aborda la resolución de los problemas clásicos de la comunicación política a partir de técnicas de análisis de uno de los bienes más preciados de nuestro tiempo: los datos. 

The Boston Police Department has limited resources and cannot easily patrol and guard the entire area under its jurisdiction. This situation limits its immediate intervention capacity to the patrolled area: forecasting dangerous criminal misconduct in a urban area throughout data analytics techniques would enable Boston Police to have better visibility on future potential crimes. For this project we are going to focus on predicting time and location of  criminal offences, with the ultimate goal to help the Boston Police Department with a model that offer meaningful insights on the crime patterns in the city.
Our data includes time, location and type of crime, weather conditions at the time and socioeconomic data of the area where it happened: these are all sourced from public entities such as the City of Boston portal, the NOAA and the US Census. To simplify the original dataset we reduced the number of possible locations by splitting Boston into a grid, defining ~ 500 areas; secondly, we summarized the 30+ different crime types into violent crimes, property crimes and other crimes, and we computed a series of time series variables accounting for the frequency of crime in each area in the previous 24 hours, week, one month and four months.
Because of the inherently different causes that might determine a violent versus a crime against property, we decided to have two separate models for forecasting the two types of crimes. In both cases, given the binary nature of the dependent variable (the occurrence of violent or property crimes for a given date and location) we developed a logistic regression and a random forest model. Specifically, as imaginable given the variable transformations described above, the model is intended to be a mixed of time series features, and “fundamentals” (income), similarly to what is done with financial models. We found that areas that have been experiencing violent crimes in the past are more likely to be affected by the same problem tomorrow. Also, the colder and the rainer outside, the less violent crimes there will be on the streets of Boston. Finally, since the average income is positively correlated and the median income is negatively correlated with the probability of both property and violent crime, this suggests that - as intuitively true - the difference between the two - representing income inequality - cause higher level of crime.
Our logistic model succeeds in delivering a basic forecasting tool that would allow Boston Police to be present - and therefore possibly deter and prevent - in 71.1% of areas in which a violent crime is happening, by patrolling only 30% of the city. Similarly, by patrolling 30% of the town, the Police would be able to be in the same area where a property crime is happening 64.9% of the times. We also tried fitting a Random Forest model on the data, but it yielded poorer results. Besides of what percentage of the city of Boston the Police is able to patrol, we can effectively interpret the ROCR curve as an optimization tool: given limited resources, the police department should give priority to patrol the locations  featuring the highest probability of crime happening in a given day. In this sense our model gives the law enforcement agencies an edge against micro criminality in the city of Boston as well as a tool to counterbalance possible budget cuts by optimizing Police presence on the ground.

Dalam makalah ini dipaparkan suatu kajian mengenai peran big data dalam riset-riset teknologi informasi terkini, pengaruh, serta potensinya dalam perkembangan ilmu komputer. Secara khusus dipaparkan mengenai peran temu balik informasi sebagai salah satu bidang riset dalam ilmu komputer untuk pengelolaan big data. Dari beberapa eksperimen yang telah dilakukan, dengan menggunakan pendekatan dari bidang ilmu temu balik informasi tersebut, dapat disampaikan empat isu besar yang menyertai perkembangan dan pengelolaan big data, yaitu: 1). infrastruktur jaringan komputer; 2). normalisasi (penyamaan) persepsi terhadap data (data science); 3). big data sebagai fondasi untuk collaborative intelligence; dan 4). perlunya kesaradaran akan nilai informasi yang mengiringi citra diri seseorang, sebuah organisasi ataupun institusi.

Crime analysis and prediction is a systematic approach for identifying the crime. This system can predict region which have high probability for crime occurrences and visualize crime prone area. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. The extraction of new information is predicted using the existing datasets. Crimes are treacherous and common social problem faced worldwide. Crimes affect the quality of life ,economic growth and reputation of nation. With the aim of securing the society from crimes, there is a need for advanced systems and new approaches for improving the crime analytics for protecting their communities. We propose a system which can analysis, detect, and predict various crime probability in given region. This paper explains various types of criminal analysis and crime prediction using several data mining techniques.

Students workshop given at National University of Singapore.

Description:
The workshop will be focused on convergences of food and ICT and various ways how to use food within everyday computer mediated communication. We are going to explore the possibilities of critical food design and talk about some novel practices within the field, such as multisensoric cooking, food sonification or data visualisations created with food. After a brief introduction into the area, we will spend some time playing with OpenSauce software and see how we can cook from colours, sounds, tweets or even human stories. We will also get our hands dirty while cooking our own data, and try to build our own edible data-visualisation prototype. And of course, we are going to eat it after all!

Requirements are descriptions of the services that a software system must provide and the constraints under which it must operate Requirements can range from high-level abstract statements of services or system constraints to detailed mathematical functional specifications Requirements Engineering is the process of establishing the services that the customer requires from the system and the constraints under which it is to be developed and operated

Data-driven journalism (DDJ) can be considered as journalism’s response to the datafication of society. To better understand the key components and development of this still young and fast evolving genre, we investigate what the field itself defines as its ‘gold-standard’: projects that were nominated for the Data Journalism Awards (DJA) from 2013 to 2016 (n= 225). Using a content analysis we examine, among other aspects, the data sources and types, visualisations, interactive features, topics, and producers. Our results demonstrate, for instance, only a few consistent developments over the years and a predominance of political pieces, of projects by newspapers and by investigative journalism organisations, of public data from official institutions as well as a glut of simple visualisations, which in sum echoes a range of general tendencies in data journalism. On the basis of our findings we evaluate DDJ’s potential for improvement with regard to journalism’s societal functions.

The not so long ago traditional way of organizing a holiday by choosing a destination seen in an album or in various brochures at a travel agency, together with the use of a travel guide and a folding map has been steadily taken over in the last years by the tourists who conduct by themselves online complex researches regarding destinations, hotels and means of transportation in order to find the best combination of solutions and rates. Nowadays, these researches are done using smartphones, tablets and other devices. At the same time, much trust is granted to peer reviews and social media. The question that arises is: Are the Romanian hoteliers spectators to these changes or are they also taking action? The present paper will try to identify the main issues Romanian hotels face currently in what concerns digital media. A special focus regards international trends and how Romanian hotels are responding to them.

The application of big data in health care is a fast-growing field, with many discoveries and methodologies published in the last five years. Big data refers to datasets that are not only big but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Moreover, medical data is one of the most growing data, as it is obtained from Electronic Health Records (EHRs) or patients themselves. Due to the rapid growth of such medical data, we need to provide suitable tools and techniques in order to handle and extract value and knowledge from these datasets to improve the quality of patient care and reduces healthcare costs. Furthermore, such value can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper presents an overview of big data content, sources, technologies, tools, and challenges in health care. It also intends to identify the strategies to overcome the challenges.

Provision of an accurate and timely disaster relief effort requires an insightful informational coverage of a crisis situation. Social media has a potential of providing a large amount of information generated by people about different aspects of a certain crisis situation. However, emergency-oriented social media utilization is still at an embryonic stage of its understanding. So, in this report 10 research studies were reviewed in an attempt to gain some understanding about this approach. As it appeared, crowdsourced information allows to get a versatile and insightful understanding of a crisis situation, but, as with any Big Data, there are major challenges of making use of high volumes of diverse information, in terms of extraction and representation of relevant and accurate information. Furthermore, other research studies proposed extraction/representation techniques which were primarily aimed at thematic and geographical clustering of information. As it appeared from the real cases of social media application during emergencies, there is still no standardized approach on how such information can be handled, but in those cases, emergency responders still managed to successfully apply their own methods of primarily geographical and thematic clustering of information, while still being greatly challenged by overwhelming amount of user-generated information. Thus, the success could perhaps be explained by social media being used primarily as a complement/enhancement of other already proved to be effective techniques. Further exploration would be needed in order to gain a more complete understanding of the current state emergency-oriented social media utilization, which could later be used as a theoretical framework for a new research study in this area.

Digital communication has changed human life since the invention of the internet. The growth of E-mail, social websites and other interpersonal communication systems in turn have brought rapid development in especially the key technological area of data analytics. Using advanced forms of analytics helps the examination of data and better informs investigative sense-making and decision-making of all kinds. The legal process called Electronic discovery (E-discovery) is used for investigating various events in the digital communication world, for the purpose of producing/obtaining evidence (such as evidence in the form of emails used in the Enron fraud case). Investigating digital communications collected over a period of time, manually, is a strenuous process, time consuming, expensive and not very effective. More recently, within E-discovery there has been development of analytics known in the legal community as “Technology assisted review” (TAR). TAR is a technologydriven assistant ...

Linked Data (LD) is a well-established standard for publishing and managing structured information on the Web, gathering and bridging together knowledge from different scientific and commercial domains. The development of Linked Data Visualization techniques and tools has been adopted as the established practice for the analysis of this vast amount of information by data scientists, domain experts, business users, and citizens.

This book covers a wide spectrum of visualization topics, providing an overview of the recent advances in this area, focusing on techniques, tools, and use cases of visualization and visual analysis of LD. It presents the core concepts related to data visualization and LD technologies, techniques employed for data visualization based on the characteristics of data, techniques for Big Data visualization, tools and use cases in the LD context, and, finally, a thorough assessment of the usability of these tools under different scenarios.

The purpose of this book is to offer a complete guide to the evolution of LD visualization for interested readers from any background and empower them to get started with the visual analysis of such data. This book can serve as a course textbook or as a primer for all those interested in LD and data visualization.

Today, data generation is exploding, and the function of marketing is becoming more sophisticated and personalized. Companies seek to gain a deeper understanding of their internal corporate environment, externalities and enhance their marketing power exponentially. Customer service is at the heart of a business's concern and has been a critical driver to leverage big data in marketing. The increasing pace of data generation has made it challenging to capture data from various sources and extract valuable business insights. Companies have taken advantage of the capabilities of big data to develop an in-depth knowledge base of their customers and increase the effectiveness of their decision-making processes. Moreover, they seek to achieve a distinct competitive advantage, deliver highly customized products or services, and stimulate innovation. Despite the increasing importance of big data in business, research investigating the potential of technology for marketing is scarce, and little attention has been paid to the role of big data in marketing activities. Therefore, the primary goal of this paper is to fill this knowledge gap and to provide a timely review that captures the dynamic nature of this field. Selected papers (n=40) were thoroughly analysed, and the potential of big data was classified in four main research themes; comprehension, competitiveness, customization, and creativity. The aim of the review is to answer the following research question: "What are the perceived benefits of big data adoption in business marketing activities?"

The Web has spanned every facet of human life and endeavors.  The penetration of web and social connectivity is crucial to development in this information age. There is a nimiety of information (data) being shared globally from the large database repositories through the World Wide Web. Semantic Web and Web 2.0 are interdependent and complimentary. The future evolution of web is hinged on Semantic Web and Web 2.0. Web 2.0 leverages on Semantic Web infrastructure to deliver mash-up (is a web page, or web application, that uses content from more than one source to create a single new service displayed in a single graphical interface)like information sharing. It is an undeniable fact that the future web applications will retain the Web 2.0 with emphasis on community and usability.

In the nearest future, there is the potential for combining Semantic Web and Web 2.0 web technologies. There are several open issues that needs to be resolved before Semantic Web and Web 2.0 can be commonplace.  Web 2.0 has largely enabled and influenced contribution to the web through simple interfaces that provide engaging interactions. There is huge amount of data (big data) that is being shared, transferred and stored across web which Semantic Web is playing a key role in data integration.

Web 2.0 has the platform for wider audience due to its participatory nature among web users. This is possible because of simple interfaces and usability by non-specialists.  Although, the data is not available for easy interlinking and re-use. Also, Web 2.0 brings about integration of heterogeneous information. On the contrary, Semantic Web lacks approachable interfaces allowing contributions, that is, it is largely closed to user contributions (i.e. non-participatory). However, the large scale data integration in Semantic Web can only be done by Specialists.
The cluster of technologies and design patterns will shape the next evolution of the Web. Semantic web provide a clear way to apply a basic level of formal semantics to the infrastructure and pages of the web. Web 2.0 deals with the design pattern of socially shared meaning while Semantic Web deals with the technologies of socially shared meaning. Therefore, the combination of Semantic Web technologies and Web 2.0 application design pattern will give rise to Social- Semantic Web also known as Web 3.0.

In a social-semantic web, certain formally representable parts of human meaning can be encoded and reasoned about via the tools of the semantic web, but can also be curated and maintained via the social, community-oriented techniques of Web 2.0.

The results of this combination would be powerful indeed. The social-semantic web promises that the subtle variations in meaning that characterize different human communities can be managed via the user-friendly collaboration mechanisms of Web 2.0, while still maintaining the expressive precision and reasoning power of the semantic Web. This would make possible a new class of applications that could leverage the semantic relations that exist between certain kinds of web-accessible data to automatically locate and fuse information, perform basic reasoning, and pivot and transform representations to meet a wide variety of user needs (Greaves and Mika, 2008).
Web 2.0 is a rich network connecting users, the Semantic Web is a network connecting data through semantic relations. The power of linking in networks increases super linearly in networks in general, and that these two networks are no exception. Interconnecting these networks is thus certain to bring value as new connections become possible within them (Greaves and Mika, 2008).
Semantic Web “bridge” technologies (like RDFa, GRDDL, and SPARQL) are used by Web 2.0 applications. Web 2.0 brings massive amounts of user-generated content and a proven recipe of humans and machines working together in synergy. Semantic Web technology will act in two principal ways: by adding structure to user data and by connecting the existing silos of data that characterize the Web 2.0 landscape. Web 2.0 applications always depend on some type of shared semantics—for example, between the software componentsof a mashup, or within the user community that contributes to a particular tagging system(Greaves and Mika, 2008).Semantic Web is a clear and well defined project while Web 2.0 is ill-defined project which lacks a clear explanation of its nature and scope (Florida, 2007).

Higher education is evolving to a data-intensive domain, with academic institutions becoming  increasingly complex from the point of view of managing and analyzing the digital information, coming from of a variety of activities, either curricular, extra-curricular, administrative or financial. Teachers and students interact with a diversity of educational resources and technologies, which are accessed by both formally enrolled but also informal continuous learners.The present paper investigates big data in the educational context, summarizing technologies, platforms, interesting use cases in regard with other related technologies, also challenges from developing a big data solution. A survey-based research was conducted among teachers and students to investigate the awareness of big data opportunity in a higher vocational university. Finally the paper presents analytical requirements for a VBL web portal as a future work.

Crime analysis and prediction is a systematic approach for identifying and analyzing patterns and trends in crime. Crime analysis is an area of vital importance to the police department. Study of crime data can help the police department to analyze crime patterns, interrelated clues and important hidden relations between the crimes, that is why data mining can be of great aid to analyze, visualize and predict crime using crime data sets. [1] Classification and correlation of data sets makes it easy to understand similarities & differences amongst the data objects. The focus is on criminality of places rather than the tracing of individual criminals. The main users of the system will be the police force who from time to time shall be able to predict the possibility of crime occurrence in the nearest future as well a particular time the crime will likely occur. In this paper we basically look at kmeans clustering algorithm for data mining and use it to generate hotspots of criminal activity and also use it to predict the chance of crime occurring in the nearest future. This analysis may help the law enforcement of the country to take a more accurate decision for example allocation of more resources like police officers in the crime prone areas [2].

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop.

Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps.

Create analytics applications by using the agile big data development methodology

Build value from your data in a series of agile sprints, using the data-value stack

Gain insight by using several data structures to extract multiple features from a single dataset

Visualize data with charts, and expose different aspects through interactive reports

Use historical data to predict the future, and translate predictions into action

Get feedback from users after each sprint to keep your project on track

This work investigates the possibility of extending the linguistic notion «Explorative Data Analysis», aiming to experimentally investigate linguistic data in order to obtain useful information more exploratively, through a spatial installation using the example of birth reports. This prototypical attempt of translating data into the physical realm raises other questions and perceptional considerations that contribute to providing a more explorative access to the data. Furthermore, the process of data handling in combination with political implications due to material considerations is thematized.

This article proposes that the practice of information visualisation (infovis), from its beginnings in the second part of the eighteenth century until today, relied on two key principles. The first principle is reduction. Infovis uses graphical primitives such as points, straight lines, curves and simple geometric shapes to stand in for objects and relations between them. The second principle is the use of spatial variables (position, size, shape and, more recently, movement) to represent key differences in the data and reveal patterns and relations. Following this analysis, the author discusses a more recent visualisation method which we can call ‘direct visualisation’ (or ‘media visualisation’): creating new visual representations from the actual visual media objects (images, video) or their parts. The article analyses the well-known examples of artistic visualisations that use this method: Listening Post (Ben Rubin and Mark Hansen), Cinema Redux (Brendan Dawes) and Preservation of Selected Traces (Ben Fry). It further suggests that direct visualisation is particularly relevant for humanities, media studies and cultural institutions. Using the actual visual artefacts in visualisation as opposed to representing them by graphical primitives helps the researcher to understand meaning and/or cause behind the patterns she may observe, as well as to discover additional patterns. To illustrate this idea, examples of projects created in the author's lab at UCSD (softwarestudies.com) are presented. Founded in 2007, the lab works on techniques and software to allow interactive exploration of large sets of visual cultural data using a direct visualisation approach and supervisualisation systems such as 215 megapixel HIPerSpace. The examples of its work are visualisations of 4553 covers of every issue of Time magazine published between 1923 and 2009; visualisations of all pages of every issue of Science and Popular Science magazines published between 1872 and 1922; and a set of visualisations of 1 million pages on Manga series.