Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 12

Analytics Related Abstracts

12 Food Supply Chain Optimization: Achieving Cost Effectiveness Using Predictive Analytics

Authors: Jayant Kumar, Aarcha Jayachandran Sasikala, Barry Adrian Shepherd

Abstract:

Public Distribution System is a flagship welfare programme of the Government of India with both historical and political significance. Targeted at lower sections of society,it is one of the largest supply chain networks in the world. There has been several studies by academics and planning commission about the effectiveness of the system. Our study focuses on applying predictive analytics to aid the central body to keep track of the problem of breach of service level agreement between the two echelons of food supply chain. Each shop breach is leading to a potential additional inventory carrying cost. Thus, through this study, we aim to show that aided with such analytics, the network can be made more cost effective. The methods we illustrate in this study are applicable to other commercial supply chains as well.

Keywords: Analytics, PDS, cost effectiveness, Karnataka, inventory cost, service level JEL classification: C53

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11 Data Management and Analytics for Intelligent Grid

Authors: G. Julius P. Roy, Prateek Saxena, Sanjeev Singh

Abstract:

Power distribution utilities two decades ago would collect data from its customers not later than a period of at least one month. The origin of SmartGrid and AMI has subsequently increased the sampling frequency leading to 1000 to 10000 fold increase in data quantity. This increase is notable and this steered to coin the tern Big Data in utilities. Power distribution industry is one of the largest to handle huge and complex data for keeping history and also to turn the data in to significance. Majority of the utilities around the globe are adopting SmartGrid technologies as a mass implementation and are primarily focusing on strategic interdependence and synergies of the big data coming from new information sources like AMI and intelligent SCADA, there is a rising need for new models of data management and resurrected focus on analytics to dissect data into descriptive, predictive and dictatorial subsets. The goal of this paper is to is to bring load disaggregation into smart energy toolkit for commercial usage.

Keywords: Data Management, Smart Grid, Analytics, Smart Utilities, energy data analytics

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10 Applications of Big Data in Education

Authors: Faisal Kalota

Abstract:

Big Data and analytics have gained a huge momentum in recent years. Big Data feeds into the field of Learning Analytics (LA) that may allow academic institutions to better understand the learners’ needs and proactively address them. Hence, it is important to have an understanding of Big Data and its applications. The purpose of this descriptive paper is to provide an overview of Big Data, the technologies used in Big Data, and some of the applications of Big Data in education. Additionally, it discusses some of the concerns related to Big Data and current research trends. While Big Data can provide big benefits, it is important that institutions understand their own needs, infrastructure, resources, and limitation before jumping on the Big Data bandwagon.

Keywords: Analytics, Big Data, Learning Analytics, Hadoop, big data in education

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9 Faculty Attendance Management System (FAMS)

Authors: G. C. Almiranez, J. Mercado, L. U. Aumentado, J. M. Mahaguay, J. P. Cruz, M. L. Saballe

Abstract:

This research project focused on the development of an application that aids the university administrators to establish an efficient and effective system in managing faculty attendance and discourage unnecessary absences. The Faculty Attendance Management System (FAMS) is a web based and mobile application which is proven to be efficient and effective in handling and recording data, generating updated reports and analytics needed in managing faculty attendance. The FAMS can facilitate not only a convenient and faster way of gathering and recording of data but it can also provide data analytics, immediate feedback system mechanism and analysis. The software database architecture uses MySQL for web based and SQLite for mobile applications. The system includes different modules that capture daily attendance of faculty members, generate faculty attendance reports and analytics, absences notification system for faculty members, chairperson and dean regarding absences, and immediate communication system concerning the absences incurred. Quantitative and qualitative evaluation showed that the system satisfactory meet the stakeholder’s requirements. The functionality, usability, reliability, performance, and security all turned out to be above average. System testing, integration testing and user acceptance testing had been conducted. Results showed that the system performed very satisfactory and functions as designed. Performance of the system is also affected by Internet infrastructure or connectivity of the university. The faculty analytics generated from the system may not only be used by Deans and Chairperson in their evaluation of faculty performance but as well as the individual faculty to increase awareness on their attendance in class. Hence, the system facilitates effective communication between system stakeholders through FAMS feedback mechanism and up to date posting of information.

Keywords: Analytics, MySQL, faculty attendance management system, SQLite, FAMS

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8 Web Page Design Optimisation Based on Segment Analytics

Authors: Varsha V. Rohini, P. R. Shreya, B. Renukadevi

Abstract:

In the web analytics the information delivery and the web usage is optimized and the analysis of data is done. The analytics is the measurement, collection and analysis of webpage data. Page statistics and user metrics are the important factor in most of the web analytics tool. This is the limitation of the existing tools. It does not provide design inputs for the optimization of information. This paper aims at providing an extension for the scope of web analytics to provide analysis and statistics of each segment of a webpage. The number of click count is calculated and the concentration of links in a web page is obtained. Its user metrics are used to help in proper design of the displayed content in a webpage by Vision Based Page Segmentation (VIPS) algorithm. When the algorithm is applied on the web page it divides the entire web page into the visual block tree. The visual block tree generated will further divide the web page into visual blocks or segments which help us to understand the usage of each segment in a page and its content. The dynamic web pages and deep web pages are used to extend the scope of web page segment analytics. Space optimization concept is used with the help of the output obtained from the Vision Based Page Segmentation (VIPS) algorithm. This technique provides us the visibility of the user interaction with the WebPages and helps us to place the important links in the appropriate segments of the webpage and effectively manage space in a page and the concentration of links.

Keywords: Analytics, Design optimization, visual block trees, vision based technology

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7 Analytics Model in a Telehealth Center Based on Cloud Computing and Local Storage

Authors: L. Ramirez, E. Guillén, J. Sánchez

Abstract:

Some of the main goals about telecare such as monitoring, treatment, telediagnostic are deployed with the integration of applications with specific appliances. In order to achieve a coherent model to integrate software, hardware, and healthcare systems, different telehealth models with Internet of Things (IoT), cloud computing, artificial intelligence, etc. have been implemented, and their advantages are still under analysis. In this paper, we propose an integrated model based on IoT architecture and cloud computing telehealth center. Analytics module is presented as a solution to control an ideal diagnostic about some diseases. Specific features are then compared with the recently deployed conventional models in telemedicine. The main advantage of this model is the availability of controlling the security and privacy about patient information and the optimization on processing and acquiring clinical parameters according to technical characteristics.

Keywords: Telemedicine, Internet of Things, Cloud Computing, Analytics

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6 Using Analytics to Redefine Athlete Resilience

Authors: Phil P. Wagner

Abstract:

There is an overwhelming amount of athlete-centric information available for sport practitioners in this era of tech and big data, but protocols in athletic rehabilitation remain arbitrary. It is a common assumption that the rate at which tissue heals amongst individuals is the same; yielding protocols that are entirely time-based. Progressing athletes through rehab programs that lack individualization can potentially expose athletes to stimuli they are not prepared for or unnecessarily lengthen their recovery period. A 7-year aggregated and anonymous database was used to develop reliable and valid assessments to measure athletic resilience. Each assessment utilizes force plate technology with proprietary protocols and analysis to provide key thresholds for injury risk and recovery. Using a T score to analyze movement qualities, much like the Z score used for bone density from a Dexa scan, specific prescriptions are provided to mitigate the athlete’s inherent injury risk. In addition to obliging to surgical clearance, practitioners must put in place a clearance protocol guided by standardized assessments and achievement in strength thresholds. In order to truly hold individuals accountable (practitioners, athletic trainers, performance coaches, etc.), success in improving pre-defined key performance indicators must be frequently assessed and analyzed.

Keywords: Injury Prevention, Analytics, athlete rehabilitation, athlete resilience, injury prediction

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5 Local Binary Patterns-Based Statistical Data Analysis for Accurate Soccer Match Prediction

Authors: Mohammad Ghahramani, Fahimeh Saei Manesh

Abstract:

Winning a soccer game is based on thorough and deep analysis of the ongoing match. On the other hand, giant gambling companies are in vital need of such analysis to reduce their loss against their customers. In this research work, we perform deep, real-time analysis on every soccer match around the world that distinguishes our work from others by focusing on particular seasons, teams and partial analytics. Our contributions are presented in the platform called “Analyst Masters.” First, we introduce various sources of information available for soccer analysis for teams around the world that helped us record live statistical data and information from more than 50,000 soccer matches a year. Our second and main contribution is to introduce our proposed in-play performance evaluation. The third contribution is developing new features from stable soccer matches. The statistics of soccer matches and their odds before and in-play are considered in the image format versus time including the halftime. Local Binary patterns, (LBP) is then employed to extract features from the image. Our analyses reveal incredibly interesting features and rules if a soccer match has reached enough stability. For example, our “8-minute rule” implies if 'Team A' scores a goal and can maintain the result for at least 8 minutes then the match would end in their favor in a stable match. We could also make accurate predictions before the match of scoring less/more than 2.5 goals. We benefit from the Gradient Boosting Trees, GBT, to extract highly related features. Once the features are selected from this pool of data, the Decision trees decide if the match is stable. A stable match is then passed to a post-processing stage to check its properties such as betters’ and punters’ behavior and its statistical data to issue the prediction. The proposed method was trained using 140,000 soccer matches and tested on more than 100,000 samples achieving 98% accuracy to select stable matches. Our database from 240,000 matches shows that one can get over 20% betting profit per month using Analyst Masters. Such consistent profit outperforms human experts and shows the inefficiency of the betting market. Top soccer tipsters achieve 50% accuracy and 8% monthly profit in average only on regional matches. Both our collected database of more than 240,000 soccer matches from 2012 and our algorithm would greatly benefit coaches and punters to get accurate analysis.

Keywords: Machine Learning, Database, Analytics, soccer

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4 Analysis of Green Wood Preservation Chemicals

Authors: Aitor Barbero-López, Soumaya Chibily, Gerhard Scheepers, Thomas Grahn, Martti Venäläinen, Antti Haapala

Abstract:

Wood decay is addressed continuously within the wood industry through use and development of wood preservatives. The increasing awareness on the negative effects of many chemicals towards the environment is causing political restrictions in their use and creating more urgent need for research on green alternatives. This paper discusses some of the possible natural extracts for wood preserving applications and compares the analytical methods available for testing their behavior and efficiency against decay fungi. The results indicate that natural extracts have interesting chemical constituents that delay fungal growth but vary in efficiency depending on the chemical concentration and substrate used. Results also suggest that presence and redistribution of preservatives in wood during exposure trials can be assessed by spectral imaging methods although standardized methods are not available. This study concludes that, in addition to the many standard methods available, there is a need to develop new faster methods for screening potential preservative formulation while maintaining the comparability and relevance of results.

Keywords: Analytics, Methods, Wood Decay, preservatives

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3 Extending Smart City Infrastructure to Cover Natural Disasters

Authors: Nina Dasari, Satvik Dasari

Abstract:

Smart city solutions are being developed across the globe to transform urban areas. However, the infrastructure enablement for alerting natural disasters such as floods and wildfires is deficient. This paper discusses an innovative device that could be used as part of the smart city initiative to detect and provide alerts in case of floods at road crossings and wildfires. An Internet of Things (IoT) smart city node was designed, tested, and deployed with collaboration from the City of Austin. The end to end solution includes a 3G enabled IoT device, flood and fire sensors, cloud, a mobile app, and IoT analytics. The real-time data was collected and analyzed using IoT analytics to refine the solution for the past year. The results demonstrate that the proposed solution is reliable and provides accurate results. This low-cost solution is viable, and it can replace the current solution which costs tens of thousands of dollars.

Keywords: Internet of Things, Analytics, natural disasters, Smart City

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2 Systematic Mapping Study of Digitization and Analysis of Manufacturing Data

Authors: R. Clancy, M. Ahern, D. O’Sullivan, K. Bruton

Abstract:

The manufacturing industry is currently undergoing a digital transformation as part of the mega-trend Industry 4.0. As part of this phase of the industrial revolution, traditional manufacturing processes are being combined with digital technologies to achieve smarter and more efficient production. To successfully digitally transform a manufacturing facility, the processes must first be digitized. This is the conversion of information from an analogue format to a digital format. The objective of this study was to explore the research area of digitizing manufacturing data as part of the worldwide paradigm, Industry 4.0. The formal methodology of a systematic mapping study was utilized to capture a representative sample of the research area and assess its current state. Specific research questions were defined to assess the key benefits and limitations associated with the digitization of manufacturing data. Research papers were classified according to the type of research and type of contribution to the research area. Upon analyzing 54 papers identified in this area, it was noted that 23 of the papers originated in Germany. This is an unsurprising finding as Industry 4.0 is originally a German strategy with supporting strong policy instruments being utilized in Germany to support its implementation. It was also found that the Fraunhofer Institute for Mechatronic Systems Design, in collaboration with the University of Paderborn in Germany, was the most frequent contributing Institution of the research papers with three papers published. The literature suggested future research directions and highlighted one specific gap in the area. There exists an unresolved gap between the data science experts and the manufacturing process experts in the industry. The data analytics expertise is not useful unless the manufacturing process information is utilized. A legitimate understanding of the data is crucial to perform accurate analytics and gain true, valuable insights into the manufacturing process. There lies a gap between the manufacturing operations and the information technology/data analytics departments within enterprises, which was borne out by the results of many of the case studies reviewed as part of this work. To test the concept of this gap existing, the researcher initiated an industrial case study in which they embedded themselves between the subject matter expert of the manufacturing process and the data scientist. Of the papers resulting from the systematic mapping study, 12 of the papers contributed a framework, another 12 of the papers were based on a case study, and 11 of the papers focused on theory. However, there were only three papers that contributed a methodology. This provides further evidence for the need for an industry-focused methodology for digitizing and analyzing manufacturing data, which will be developed in future research.

Keywords: Manufacturing, Analytics, Digitization, Industry 4.0

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1 Reference Architecture for Intelligent Enterprise Solutions

Authors: Shankar Kambhampaty, Harish Rohan Kambhampaty

Abstract:

Data in IT systems in enterprises has been growing at a phenomenal pace. This has provided opportunities to run analytics to gather intelligence on key business parameters that enable them to provide better products and services to customers. While there are several artificial intelligence (AI/ML) and business intelligence (BI) tools and technologies available in the marketplace to run analytics, there is a need for an integrated view when developing intelligent solutions in enterprises. This paper progressively elaborates a reference model for enterprise solutions, builds an integrated view of data, information, and intelligence components, and presents a reference architecture for intelligent enterprise solutions. Finally, it applies the reference architecture to an insurance organization. The reference architecture is the outcome of experience and insights gathered from developing intelligent solutions for several organizations.

Keywords: Artificial Intelligence, Business Intelligence, Enterprise, Architecture, Analytics, Intelligence, model

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