Data Analytics

Data analytics (DA) is the process of examining data sets in order to draw conclusions
about the information they contain, increasingly with the aid of specialized systems and
software. Data analytics technologies and techniques are widely used in commercial industries to
enable organizations to make more-informed business decisions and by scientists and researchers
to verify or disprove scientific models, theories and hypotheses. (Rouse and Stedman, 2016).
This project will seek to discover the applicability of DA within higher education and
specifically, the usability of DA to the University of the West Indies (UWI). By using DA, the
UWI can achieve many business outcomes and be more responsive to their target market.

Artificial intelligence (AI) is reshaping the organizations and society because of its pervasiveness capacity. AI refers to machines (robots, computers, etc.) making tasks that typically demand specific human intelligence. Moreover, AI with its sub-field 'Machine Learning' (ML) is the core of the new era of Data-oriented Manufacturing 4.0 of nowadays. For industrials, these two paradigms are becoming an absolute critical key to success to remain competitive. AI approaches are the most enabler techniques of the 'intelligent' industry through process automation, industrial robotics, predictive decision-aid, Big Data cyber-integration, cyber-physical systems, IoT connectivity, and so forth. In AI-powered manufacturing, the traditional theory-oriented management is being replaced by Data-oriented management where Data is taking the central in guiding processes 'intelligently' instead of old best practices. Therefore, mutations are already in their way of affecting completely the industrial worldwide networks by eliminating inefficiencies, assisting workers, and creating new opportunities, but also in engendering unexpected challenges by altering the current operations. The objective of this special session is to call academics to debate and examine the way AI approaches are influencing the industrial processes under the new era of Manufacturing 4.0.

In a recent short report, the necessity of sophisticated practices in gathering records that would facilitate data sharing yields data-driven analysis in time of COVID-19. Consequently, there is a need to present the truth in data analytics in the era of COVID-19. This paper discusses the urgent call for people handling the COVID-19 data to be ethically responsible in their handling, processing, and reporting that impacts the lives of ordinary people especially in this time of pandemic as public health crisis.

A central challenge to educational videogame research is capturing salient in-game data on play and learning. ADAGE
(Assessment Data Aggregator for Game Environments) is a click-stream data framework currently being developed by
the Games+Learning+Society research center to facilitate standardized collection of embedded assessment data across games. ADAGE integrates core game design structures into a
click-stream data (telemetry) schema, which is then seeded with context vital to informing learning analyses. These data can be used to identify patterns in play within and across players (using data mining and learning analytic techniques) as well as statistical methods for testing hypotheses that compare play to content models. Three provided analysis examples show this diversity, combining applied statistics with Markov modeling and classification tree visualization. Overall, ADAGE provides a
standardized game telemetry framework with a rich, method-agnostic data yield, efficient enough to have scalability, and flexible enough to use across games.

Abstract With the proliferation of smart meters in smart grids, new challenges have emerged in the energy sector and applications are continuously developed, mainly concerning data analytics to address those challenges. Traditionally, data analytics in smart grid systems is performed in server-side tier; however, it is necessary to process data analytics close to the smart meter to achieve better performance. In order to process data effectively, it is also necessary to implement methodologies to facilitate the integration of data analysis processes in the Advanced Metering Infrastructure (AMI). This paper presents a novel architecture for data analytics in Smart Metering Systems based on an edge-fog-cloud computing architecture that permits different types of data analytics in a multi-tier context. The proposed architecture has the capability of learning and adapting to different contexts in smart metering systems using a reinforcement learning approach. The architecture was tested with three different analytic applications: forecasting energy consumption, prediction of power quality and prediction of energy theft. The results indicate that the methodology can be feasible solution for direct implementation in Smart Metering Systems.

Data warehouses (DW) play a decisive role in providing analytical information for decision making. Multidimensional modeling is a special approach to modeling data, considered the foundation for building data warehouses. With the explosive growth in the amount of heterogeneous data (most of which external to the organization) in the latest years, the DW has been impacted by the need to interoperate and deal with the complexity of this new type of information, such as big data, data lakes and cognitive computing platforms, becoming evident the need to improve the semantic expressiveness of the DW. Research has shown that ontological theories can play a fundamental role in improving the quality of conceptual models, reinforcing their potential to support semantic interoperability in its various manifestations. In this paper we propose the application of ontological patterns, grounded in the Unified Foundational Ontology (UFO), for conceptual modeling in multidimen-sional models, in or...

The Customer Experience is a crucial key aspect for the function of companies. It ensures the trust between the customer and the company itself. Moreover it enables the organisation to improve by analyzing the comments from feedback, in order to comprehend customers and keep them close to the company or attract further by spreading good rumors to the market. However, these technologies are new and they begin integrating now in the functions of organisations. Hence a lot of studies have recently been conducted for proposing methods to improve different aspects of the organisation with the help of big data analytics. There are a lot of factors which should be considered carefully and put into consideration. Due to the complexity of the problem, different fields of science have to be coordinated e.g. social sciences, maths, software, electronics. In this article, it is presented a review from some papers on Big Data analytics, which emphasize on Customer Experience and they describe the methods to extract data from customers and process them efficiently with automated procedures.

Fault detection and prediction in the semiconductor industry will enable organizations to optimize their resources and maintain a high production yield for semiconductors. However, the data generated from the sensors are producing an abundance amount of data being kept in the repository and it is difficult for process engineers to analyze and make use of the dataset to make timely and accurate fault prediction and detection. Hence, this research will focus on developing a predictive model to predict and detect faulty equipment in the semiconductor manufacturing process. The quality of the data is enhanced through various analyses, feature selection and dimensionality reduction techniques. Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbor (KNN) and Artificial Neural Network (ANN) will be the main algorithms used to develop the predictive models and their respective results compared to determine the best model to be used.

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing. This is an open access article under the CC BY-SA license.

In a recent short report, the necessity of sophisticated practices in gathering records that would facilitate data sharing yields data-driven analysis in time of COVID-19. Consequently, there is a need to present the truth in data analytics in the era of COVID-19. This paper discusses the urgent call for people handling the COVID-19 data to be ethically responsible in their handling, processing, and reporting that impacts the lives of ordinary people especially in this time of pandemic as public health crisis.

Behavioral Economics is defined as a method of economic analysis that applies psychological insights into human behavior to explain economic decision-making. It mainly focuses on the effect of emotions, opinions, norms and habits on the process of decision-making and it supports that humans are characterized by a bounded rationality, which means that people are not fully rational when they make choices, either due to heuristics and biases or due to information asymmetries, lack of attention, limited working memory… The list is endless. A rational person is someone who has self-control and decides based on logic and reason, after considering the costs and benefits that are linked to a particular choice.

The aim of this paper is to prove how relevant the insights of Behavioral Economics are for Data Protection Law, particularly regarding the complex phenomenon of Internet Profiling. Effective manipulation techniques are adopted by marketers and service providers in order to urge internet users to disclose personal information and to guide them to specific decisions. These techniques are based on the exploitation of human frailties. The same human frailties are to blame for users waiving their data rights, despite the fact that they are concerned about their privacy. Because of these human weaknesses, people disclose data which is then processed, analyzed and used for the construction of data profiles. These data profiles reveal even more personal information about them and thus enable even more effective manipulation to take place, now in the form of targeted advertisements.

In order to examine the complex phenomenon of Internet Profiling, a sociolegal approach will be adopted and a thorough analysis will be made. In the first section, I will try to define internet profiling and simplify its concept as far as possible. The role of Artificial Intelligence and Algorithms in relation to the processing and the analysis of data will be examined. The second section will expose the ways data is distributed with a main focus on the intraoperable way, according to which a huge amount of data is accumulated under the control of a sole data controller. Two examples of such data controllers (Google and Facebook) will be given in order to show how many sources of data (data points) they have, by virtue of their expansion strategies.

In the next section, I will go through an analysis of all the problematic aspects of internet profiling. The first part will pinpoint that Artificial Intelligence and algorithms are not infallible in an effort to debunk the myth of the perfection of computing systems. The side-effects of the system’s errors will be examined. The second part will mainly deal with the way those who control the datasets use their power in order to avail themselves, with a central focus on “nudging”, a manipulation technique which takes advantage of human’s heuristics and biases. Apart from the consequences of this tactic on several levels, an assessment of the technique on grounds of effectiveness will also take place. In the third part, a discussion about autonomy and  privacy issues will take place, while in the final part, the theory of propertization of data and the proposal of paying with money instead of data for the use of certain services will be evaluated.

The next part will present the legal framework of Internet profiling. Firstly, an examination of the legal term of “concept” will take place, since it plays a central role in Data Protection Law and in the Cookies Directive, according to which users must consent to the tracking of cookies at their devices. The requirements for a consent to be valid will be cited. Secondly, the human frailties which are to blame for users’ giving their consent will be outlined. Last but no least, the implications of Article 22 of GDPR, regarding automated decision-making and thus of great relevance for internet profiling, will be presented.

The development of technology has become inevitable in almost every sphere. Over the years technology has permeated even in the defence technology. A dramatic transformation of war took place in the year 1945 with the development of nuclear weapons. Since then, there was no looking back. The nation states have come a long way with the development of gunpowder, nuclear weapons and now with the consideration of developing Lethal Autonomous Weapons System it has moved towards fourth phase of Industrial Revolution. The Lethal Autonomous Weapons System is considered to be such a system which is in the phase of an ongoing development in the field of military technology. However, the transformation in the conduct of wars is not without its detrimental effects. Also, the development of weapons takes place not because the nations are interested to develop, but because of the prestige and the deterrent theory. The development of such technologically advanced weapons which can identify, attack and destroy targets without human intervention is beyond the permissible limits of International Human Rights Law and the Constitutional Standards of Domestic Law. The paper condemns the development of such technologically advanced lethal autonomous weapons system as a conduct of war methodology. The author further emphasizes the limits of the International Law vis-à-vis the Constitutional limits of Domestic Law specifically, India.

Forged documents specifically passport, driving licence and VISA stickers are used for fraud purposes including robbery, theft and many more. So detecting forged characters from documents is a significantly important and challenging task in digital forensic imaging. Forged characters detection has two big challenges. First challenge is, data for forged characters detection is extremely difficult to get due to several reasons including limited access of data, unlabeled data or work is done on private data. Second challenge is, deep learning (DL) algorithms require labeled data, which poses a further challenge as getting labeled is tedious, time-consuming, expensive and requires domain expertise. To end these issues, in this paper we propose a novel algorithm, which generates the three datasets namely forged characters detection for passport (FCD-P), forged characters detection for driving licence (FCD-D) and forged characters detection for VISA stickers (FCD-V). To the best of our kn...

This research focuses on providing insights for a solution for collecting, storing, analyzing and visualizing data from customer energy consumption patterns. The data analysis part of our research provides the models for knowledge discovery that can be used to improve energy efficiency at both producer and consumer ends. Τhe study sets a new analytical framework for assessing the role of behavioral knowledge in energy efficiency using a wide range of Case Studies, Experiments, Research, Information and Communication Technologies (ICT) in combination with the most modern econometric methods and large analytical data taking into account the characteristics of the study participants (household energy customers).

Nowadays, analytics is playing a key role in any field. Analytics is used in our day to day life. People don’t want to purchase even without analytics. Before few months IPL (Indian Premier League for Cricket) auction was taking placed. Even franchises are interested to pick a cricket player based on their past performances. For all this, they need accurate data and analytics with the comparative study to pick a particular player for their team. Here, we are comparing the last 10 years cricket sports data of IPL with interesting facts. We can perform much analytics but here we are focusing on the comparison between popular players, team, and fielders. Performance of top-10 players in each category like batting, bowling and fielding. Valuable player for the specific team.

Knowledge-based decision support systems (KB-DSS) recommend decision actions based on stored knowledge. One of the challenges in time-critical clinical KB-DSS is the ability to maintain and continually improve the underlying knowledge-base. This challenge stems from the dynamic nature of the knowledge, and from the time constraints associated with the time critical interventions which do not afford for a traditional knowledge engineering approaches to feasibly occur during actual system usage. This paper explores the proposition that a usage-driven design approach could act as an enabler for the continual improvement of time critical, real-time KB DSS. We will describe a case study of an existing trauma reception and resuscitation time-critical real-time decision support system to express the challenges found in the current implementation and define the information requirements for supporting the proposed approach using a design-science-research methodology.

Purpose: The research aims to characterize newest soultions, especially with the respect to cybersecurity aspects of Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data. Design/Methodology/Approach: The working hypothesis refers to assumption that current regulations and security solutions for Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data is under extreme preassure to meet evergrowing challenges. There are more and more dends form the legal regulators as well as from the market and that creates a lot of problems with data protection. The article uses legal and comparative analysis as well as structural and functional analysis. Additionally, the interpretation method is also present. Findings: Article indicates that the aforementioned issues with the respect to growing importance of internet including the Internet of Things and Internet of Everything are becoming of more and more importance and cannot go with appropriate level of cybersecurity since the data they collect is of the great importance. The trends immanent to Industry 4.0 require from business more effort and customer orientation. Growing population and access to Internet demands larger scales of business operations. Practical Implications: As a result of conducting the research, it is possible to identify threats and present some recommendations for cybersecurity of Business Intelligence. Originality/Value: This is a complete research for Cybersecurity of Business Intelligence analytics based on the processing of large sets of information with the use of sentiment analysis and Big Data.

Keywords Disclaimer: *Data shared by the customer is confidential and sensitive, it should not be used for any purposes apart from capstone project submission for PGA. The Name and demographic details of the enterprise is kept confidential as per their owners' request and binding.

The application of data mining has been utilized in different fields ranging from agriculture, finance, education, security, medicine, research etc. Data mining derives useful information from careful examination of data. In Nigeria, Agriculture plays a critical role in the economy as it provides high level of employment for many people. It is typical of farmers in Nigeria to plant crops without paying considerate attention to the soil and crop requirements as most farmers inherit the lands used for farming from their fathers and just continue in the pattern of farming they had always known. This is not favorable in the level of productivity they can actually attain as the effect can be seen in same level of crop yield year after year if not even worse. Modern farming techniques make use of data mining from previous data considering soil types, and other factors like weather and climatic conditions. This study built a model that enables possible prediction of crop yield from the his...

Data is everywhere. Ubiquitously, companies and governments are collecting data. One simple visit to the World Wide Web leaves a wide digital footprint. One Google search about the best mutual funds in the market and you will be bombarded with mutual fund advisories anywhere you visit. The lines between the online world and the offline world are blurring, as the number of metadata increases. This paper looks at certain ways as to how our civilization is becoming smarter, with every device around us right from the mobile phone to electric bulb, becoming smarter with each passing day. This paper looks at the origins of the collection of metadata, as well as their use in customizing the world as per our needs and wants.

It is an important strategy for grocery stores to forecast sales, that knowing how many sales are needed in advance to avoid overstocking or understocking. In this report, grocery sales data from Ecuadorian supermarket chain ‘Corporacion Favorita’ on Kaggle platform was extensively analysed, with the aim to predict and forecast product sales for their stores.

This project investigated analytical techniques used by organizations across many industries for HR functions, and how data analytical techniques can help HR departments work efficiently. The goal was to look into the obstacles and opportunities that companies have when using HR analytics as a tool in their businesses. This project used secondary data acquired from earlier research articles, journals from the years 2016 to 2019, blogs, and websites to investigate theories and applications of HR analytics. It also examined the need of analytics to assist HR leaders in thinking about the implications of these technologies in future work and how data analytics will shape the HR system in the future. The project examined the implementation of different AI and Data Analytics techniques - Predictive, Perspective, Descriptive and Diagnostic analytics that are already in use by organizations to maximize efficiency in the HR department. The project also uncovered the following important issu...

The project E-Kart.com.com is a web-based application. The Shopping Cart program allows visitors to visit the on-line shopping site to collect items in a virtual shopping cart. They may view the contents of their shopping cart at any time and may add or delete items at will. The program automatically calculates the subtotal, sales tax, shipping charges, and grand total. When a visitor decides to check-out, the order information including the buyer's name, address and billing instruction is e-mailed to your order department and a receipt is sent to the shopper.

This study addresses the healthcare services problems which focus on the upcoming and promising areas of medical research and proposed a novel approach integrating in big data analytics and Apache. The proposed approach will improve the healthcare services fastly and efficiently. The big data analytics can continually evaluate clinical data in order to improve the effective practices of physicians and improved patient care

Social Networking Sites such as twitter and Facebook is growing in large number thus allowing general masses to share views amongst each other regarding social, entertainment, political, marketing issues. However this heap of data is in unstructured format and thus cannot be used for identifying public opinions. This paper presents a methodological approach to extract meaningful data from popular microblogging website like twitter. The majority opinion state is investigated and realized on the basis of polarity of the words shared in communication. The timely identification of the dominated opinion of people can be useful and employed indiverse areas such as marketing of product, entertainment promotions etc. INTRODUCTION With the advancement in technology, communication has witnessed widened horizons. Thishas facilitated economical ways to communicate and connect with people across the world. The distance is no longer a factor for difficulties in communication, this is all attributed to social media and networking. Social media has provided a platform for exchange of ideas, views, and feelings around the world just by the help of internet hence decreasing the money spent on communication through letters, calls from landline, mobile phones etc. It has also made sharing of information, pictures, audios, videos effortless. Social networks provide individuals with an account to keep and manage. Nearly 10TB of data is generated and spread through these social networks which have attracted huge interests from the research community and industry and lots of useful information can be gathered from this huge data. The Sentiments that people express can be used for doing a lot of research in marketing, education and other famous industries due to which sentiment analysis came into existence. Sentiment analysis is a way of classifying the opinions that are mined using opinion mining in positive, negative and neutral statement of opinion holder. Sentiment analysis is developing exponentially with numerous emerging approaches targeting the recognition of sentiment, reflected in written language [1]. The main problem of data which is collected from social network is that it is in unstructured form which makes it difficult to identify public opinion. To our knowledge the data gathered from social media and analyzed does not take dominated views into consideration In our paper we would be extracting useful data from a microblogging site like Twitter as it is open and people only use it to express their views only by the use of 140 characters per post then we would be finding out the sentiments of those opinions by the use of code developed in python and then provide the overall opinion of public about a topic.

The rapid advancement in artificial intelligence, semiconductor and electronic technology, software engineering and programming techniques as well as hardware interfaces and communication has raised the demand for advancement in operating system. In this article we have studied the classical and current operating systems and have proposed the advanced components such as new user interfaces, advanced human-to-machine interfaces, communication, artificial intelligence and advanced hardware technology etc. which are necessary for next generation operating systems.

Predicting and forecasting the financial markets has always been seen as a ‘dark art’, secretive trading strategies known only to a few. Financial investment and trading strategies often reflect a plethora of expert decisions, calculation and predictions to maximize the*probability of choosing a profitable trade.

Based on mathematics and engineering point of view, we aim to explore the establishment of model structure, and thus calculate the benefits and risks of a financial product. Specifically, we exploit a large amount of market data of futures options so as to address two issues in this work. The first issue is to discover an appropriate product choice and timing for profitable trading. Without loss of generality, we investigate the effectiveness of several trading constraints and technical indicators by scrutinizing and back testing with the long-term market data. The second issue is to use the spread strategies for risk control when being an options seller. Note that a spread position is constituted where one buys an option and sells another option against it. In general, we develop a scheme to simulate different trading strategies and thus identify some simple but profitable strategies. Experimental studies show that our strategies yield good profit in the TAIFEX market during 2009 to 2018.

ABSTRACT Design strategies that account for local micro-climatic conditions can reduce the overall energy consumption of buildings over its life. This paper evaluates non-structural retrofit strategies for reducing energy consumption for existing buildings. Based on real-life data on usage statistics of buildings and accounting for local climatic conditions, we validate energy consumption predictions by Sefaira® building simulation software. A comparative analysis is then performed for various energy conservation scenarios by estimating energy reduction and payback periods. Our analysis shows that various retrofit strategies can lead to reduction in energy consumption upto 60% with a pay back period of about 9 years.

People Analytics (PA) consists of applying statistics and behavioural sciences to people management to obtain competitive business advantages. If any, the boundaries that distinguish this discipline from neighbouring ones as HR Analytics and Workforce Analytics are unclear. Regardless, in the current bibliography, you can find references both to the benefits of using PA working on the different Human Resources (HR) functions, as well as to the benefits of its application in other business units. This master's thesis seeks to understand what People Analytics is, explore if its presence in Spain differs from other markets, if there is a knowledge gap between HR and PA professionals, learn about contexts of use, tools and advantages; and identify the best training to become a People Analyst. To verify this, a form has been administered to a sample of forty-four professionals with a prior interest in People Analytics, and four of the participants, professionally engaged in it, have been interviewed. Subsequently, the results have been analysed quantitatively and qualitatively, respectively. To conclude, the results are contrasted with each other, and the information collected in the theoretical framework worked, thus giving answers to the research questions derived from the initial objectives.

IBM Visual Recognition utiliza algoritmos de deep learning para analizar imágenes de escenas, objetos, texto, entre otros elementos.

Realizar hallazgos y construir una narrativa adecuada para comunicarlos son habilidades fundamentales en Ciencia de Datos. El principal ingrediente es la curiosidad.