Relevant bibliographies by topics / Analytics Application

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Analytics Application'

Author: Grafiati
Published: 4 June 2021
Last updated: 15 February 2022

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Journal articles on the topic "Analytics Application"

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G, Aravind, Varun K, and Manjunath C. R. Soumya K. N. "Application of Big Data Analytics with Evidence Based Medicine." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 440–44. http://dx.doi.org/10.31142/ijtsrd12979.

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Nagaraj, Samala. "Marketing Analytics for Customer Engagement." International Journal of Information Systems and Social Change 11, no. 2 (April 2020): 41–55. http://dx.doi.org/10.4018/ijissc.2020040104.

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Customer engagement is the buzz word in marketing discipline today. Engaging customers has never been as effective before the emergence of marketing analytics and its application. Marketing analytics coupled with social media and brand communities has given rise to improved innovative ways to engage customers across various service industries. The integration of marketing analytics with artificial intelligence (AI) has enhanced marketers understanding of customer engagement. The present article is a viewpoint on the various applications of marketing analytics for customer engagement. The present article focuses on the evolution of marketing analytics, its various models and application in various forms of customer engagement. The article highlights the future applications of analytics and concludes with the importance of marketing analytics for marketers in increasing customer engagement.
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Agrawal, Deepak. "Analytics based decision making." Journal of Indian Business Research 6, no. 4 (November 11, 2014): 332–40. http://dx.doi.org/10.1108/jibr-09-2014-0062.

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Purpose – This paper aims to trace the history, application areas and users of Classical Analytics and Big Data Analytics. Design/methodology/approach – The paper discusses different types of Classical and Big Data Analytical techniques and application areas from the early days to present day. Findings – Businesses can benefit from a deeper understanding of Classical and Big Data Analytics to make better and more informed decisions. Originality/value – This is a historical perspective from the early days of analytics to present day use of analytics.
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Ruipérez-Valiente, José, Pedro Muñoz-Merino, Díaz Pijeira, Ruiz Santofimia, and Carlos Kloos. "Evaluation of a learning analytics application for open edX platform." Computer Science and Information Systems 14, no. 1 (2017): 51–73. http://dx.doi.org/10.2298/csis160331043r.

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Massive open online courses (MOOCs) have recently emerged as a revolution in education. Due to the huge amount of users, it is difficult for teachers to provide personalized instruction. Learning analytics computer applications have emerged as a solution. At present, MOOC platforms provide low support for learning analytics visualizations, and a challenge is to provide useful and effective visualization applications about the learning process. At this paper we review the learning analytics functionality of Open edX and make an overview of our learning analytics application ANALYSE. We present a usability and effectiveness evaluation of ANALYSE tool with 40 students taking a Design of Telematics Applications course. The survey obtained very positive results in a system usability scale (SUS) questionnaire (78.44/100) in terms of the usefulness of visualizations (3.68/5) and the effectiveness ratio (92/100) of the actions required for the respondents. Therefore, we can conclude that the implemented learning analytics application is usable and effective.
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Pike, William, Joe Bruce, Bob Baddeley, Daniel Best, Lyndsey Franklin, Richard May, Douglas Rice, Rick Riensche, and Katarina Younkin. "The Scalable Reasoning System: Lightweight Visualization for Distributed Analytics." Information Visualization 8, no. 1 (January 2009): 71–84. http://dx.doi.org/10.1057/ivs.2008.33.

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A central challenge in visual analytics is the creation of accessible, widely distributable analysis applications that bring the benefits of visual discovery to as broad a user base as possible. Moreover, to support the role of visualization in the knowledge creation process, it is advantageous to allow users to describe the reasoning strategies they employ while interacting with analytic environments. We introduce an application suite called the scalable reasoning system (SRS), which provides web-based and mobile interfaces for visual analysis. The service-oriented analytic framework that underlies SRS provides a platform for deploying pervasive visual analytic environments across an enterprise. SRS represents a ‘lightweight’ approach to visual analytics whereby thin client analytic applications can be rapidly deployed in a platform-agnostic fashion. Client applications support multiple coordinated views while giving analysts the ability to record evidence, assumptions, hypotheses and other reasoning artifacts. We describe the capabilities of SRS in the context of a real-world deployment at a regional law enforcement organization.
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Ghosh, Siddhartha, Akshat Agrawal, B. Ramu, S. Kishore Kumar, and N. Tharun Reddy. "Application of C in Data Analytics." Journal of Engineering Education Transformations 33 (January 31, 2020): 600. http://dx.doi.org/10.16920/jeet/2020/v33i0/150128.

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Kim, Jeong-ryeol, and Je-Young Lee. "English Learning Analytics and its Application." Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology 6, no. 9 (September 30, 2016): 321–30. http://dx.doi.org/10.14257/ajmahs.2016.09.15.

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Goncalves, Carlos, Luis Assuncao, and Jose C. Cunha. "Flexible MapReduce Workflows for Cloud Data Analytics." International Journal of Grid and High Performance Computing 5, no. 4 (October 2013): 48–64. http://dx.doi.org/10.4018/ijghpc.2013100104.

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Data analytics applications handle large data sets subject to multiple processing phases, some of which can execute in parallel on clusters, grids or clouds. Such applications can benefit from using MapReduce model, only requiring the end-user to define the application algorithms for input data processing and the map and reduce functions, but this poses a need to install/configure specific frameworks such as Apache Hadoop or Elastic MapReduce in Amazon Cloud. In order to provide more flexibility in defining and adjusting the application configurations, as well as in the specification of the composition of the application phases and their orchestration, the authors describe an approach for supporting MapReduce stages as sub-workflows in the AWARD framework (Autonomic Workflow Activities Reconfigurable and Dynamic). The authors discuss how a text mining application is represented as a complex workflow with multiple phases, where individual workflow nodes support MapReduce computations. Access to intermediate data produced during the MapReduce computations is supported by a data sharing abstraction. The authors describe two implementations of this abstraction, one based on a shared tuple space and another based on an in-memory distributed key/value store. The authors describe the implementation of the framework, a set of developed tools, and our experimentation with the execution of the text mining algorithm over multiple Amazon EC2 (Elastic Compute Cloud) instances, and report on the speed-up and size-up results obtained up to 20 EC2 instances and for different corpus sizes, up to 97 million words.
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KIM, JENNIFER, DAVID A. OSTROWSKI, HIROSHI YAMAGUCHI, and PHILLIP C. Y. SHEU. "SEMANTIC COMPUTING AND BUSINESS INTELLIGENCE." International Journal of Semantic Computing 07, no. 01 (March 2013): 87–117. http://dx.doi.org/10.1142/s1793351x13500013.

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With rapidly expanding data collections becoming increasingly available, the application of Semantic Computing has become imperative to leverage this resource for industrial applications. This paper presents a survey of Semantic Computing in the area of Business Intelligence. We examine semantic analytical techniques and tools as applied for prediction analysis and decision support. We also define the role of Semantic Computing as applied in the context of Data Mining, Text Mining and Big Data Analytics. Additionally, we describe how business data is queried with Structured Natural Language as well as the use of On-Line Analytic Processing.
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Wise, Alyssa, Yuting Zhao, and Simone Hausknecht. "Learning Analytics for Online Discussions: Embedded and Extracted Approaches." Journal of Learning Analytics 1, no. 2 (August 7, 2014): 48–71. http://dx.doi.org/10.18608/jla.2014.12.4.

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This paper describes an application of learning analytics that builds on an existing research program investigating how students contribute and attend to the messages of others in asynchronous online discussions. We first overview the E-Listening research program and then explain how this work was translated into analytics that students and instructors could use to reflect on their discussion participation. Two kinds of analytics were designed: some embedded in the learning environment to provide students with real-time information on their activity in-progress; and some extracted from the learning environment and presented to students in a separate digital space for reflection. In addition, we describe the design of an intervention though which use of the analytics can be introduced as an integral course activity. Findings from an initial implementation of the application indicated that the learning analytics intervention supported changes in students’ discussion participation. Five issues for future work on learning analytics in online discussions are presented. One, unintentional versus purposeful change; two, differing changes prompted by the same analytic; three, importance of theoretical buy-in and calculation transparency for perceived analytic value; four, affective components of students’ reactions; and five, support for students in the process of enacting analytics-driven changes.
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Dissertations / Theses on the topic "Analytics Application"

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Talevi, Iacopo. "Big Data Analytics and Application Deployment on Cloud Infrastructure." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14408/.

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This dissertation describes a project began in October 2016. It was born from the collaboration between Mr.Alessandro Bandini and me, and has been developed under the supervision of professor Gianluigi Zavattaro. The main objective was to study, and in particular to experiment with, the cloud computing in general and its potentiality in the data elaboration field. Cloud computing is a utility-oriented and Internet-centric way of delivering IT services on demand. The first chapter is a theoretical introduction on cloud computing, analyzing the main aspects, the keywords, and the technologies behind clouds, as well as the reasons for the success of this technology and its problems. After the introduction section, I will briefly describe the three main cloud platforms in the market. During this project we developed a simple Social Network. Consequently in the third chapter I will analyze the social network development, with the initial solution realized through Amazon Web Services and the steps we took to obtain the final version using Google Cloud Platform with its charateristics. To conclude, the last section is specific for the data elaboration and contains a initial theoretical part that describes MapReduce and Hadoop followed by a description of our analysis. We used Google App Engine to execute these elaborations on a large dataset. I will explain the basic idea, the code and the problems encountered.
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Altskog, Tomas. "Customized Analytics Software : Investigating efficient development of an application." Thesis, Mittuniversitetet, Avdelningen för informations- och kommunikationssystem, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-27967.

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Google Analytics is the most widely used web traffic analytics program in the world with a wide array of functionality which serve several different purposes for its users. However the cost of training employees in the usage of Google Analytics can be expensive and time consuming due to the generality of the software. The purpose of this thesis is to explore an alternative solution to hav- ing employees learn the default Google Analytics interface and thus possibly re- ducing training expenses. A prototype written in the Java programming lan- guage is developed which implements the MVC and facade software patterns for the purpose of making the development process more efficient. It contains a feature for retrieving custom reports from Google Analytics using Google’s Core Reporting API in addition to two web pages are integrated into the proto- type using the Google Embed API. In the result the prototype is used along with the software estimation method COCOMO to make an estimation of the amount of effort required to develop a similar program. This is done by counting the prototype’s source lines of code manually, following the guidelines given by the COCOMO manual, and then implementing the result in the COCOMO estima- tion formula. The count of lines of code for the entire prototype is 567 and the count which considers reused code is 466. The value retrieved from the formula is 1.61±0.14 person months for the estimation of the entire program and 1.31± 0.16 for a program with reused code. The conclusion of the thesis is that the res- ult from the estimation has several weaknesses and further research is necessary in order to improve the accuracy of the result.
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Lee, Hock Guan. "A study on predictive analytics application to ship machinery maintenance." Thesis, Monterey California. Naval Postgraduate School, 2013. http://hdl.handle.net/10945/37659.

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Approved for public release; distribution is unlimited
Engine failures on ships are expensive, and affect operational readiness critically due to long turn-around times for maintenance. Prior to the engine failures, there are signs of engine characteristic changes, for example, exhaust gas temperature (EGT), to indicate that the engine is acting abnormally. This is used as a precursor towards the modeling of failures. There is a threshold limit of 520 degree Celsius for the EGT prior to the need for human intervention. With this knowledge, the use of time series forecasting technique, to predict the crossing over of threshold, is appropriate to model the EGT as a function of its operating running hours and load. This allows maintenance to be scheduled just in time. When there is a departure of result from the predictive model, Cumulative Sum (CUSUM) Control charts can then be used to monitor the change early before an actual problem arises. This paper discusses and demonstrates the proof of principle for one engine and a particular operating profile of a commercial vessel with the use of predictive analytics. The realization with time series forecasting coupled with CUSUM control chart allows this approach to be extended to other attributes beyond EGT.
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Mathonat, Romain. "Rule discovery in labeled sequential data : Application to game analytics." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI080.

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Exploiter des jeux de données labelisés est très utile, non seulement pour entrainer des modèles et mettre en place des procédures d'analyses prédictives, mais aussi pour améliorer la compréhension d'un domaine. La découverte de sous-groupes a été l'objet de recherches depuis deux décennies. Elle consiste en la découverte de règles couvrants des ensembles d'objets ayant des propriétés intéressantes, qui caractérisent une classe cible donnée. Bien que de nombreux algorithmes de découverte de sous-groupes aient été proposés à la fois dans le cas des données transactionnelles et numériques, la découverte de règles dans des données séquentielles labelisées a été bien moins étudiée. Dans ce contexte, les stratégies d'exploration exhaustives ne sont pas applicables à des cas d'application rééls, nous devons donc nous concentrer sur des approches heuristiques. Dans cette thèse, nous proposons d'appliquer des modèles de bandit manchot ainsi que la recherche arborescente de Monte Carlo à l'exploration de l'espace de recherche des règles possibles, en utilisant un compromis exploration-exploitation, sur différents types de données tels que les sequences d'ensembles d'éléments, ou les séries temporelles. Pour un budget temps donné, ces approches trouvent un ensemble des top-k règles decouvertes, vis-à-vis de la mesure de qualité choisie. De plus, elles ne nécessitent qu'une configuration légère, et sont indépendantes de la mesure de qualité utilisée. A notre connaissance, il s'agit de la première application de la recherche arborescente de Monte Carlo au cas de la fouille de données séquentielles labelisées. Nous avons conduit des études appronfondies sur différents jeux de données pour illustrer leurs plus-values, et discuté leur résultats quantitatifs et qualitatifs. Afin de valider le bon fonctionnement d'un de nos algorithmes, nous proposons un cas d'utilisation d'analyse de jeux vidéos, plus précisémment de matchs de Rocket League. La decouverte de règles intéressantes dans les séquences d'actions effectuées par les joueurs et leur exploitation dans un modèle de classification supervisée montre l'efficacité et la pertinence de notre approche dans le contexte difficile et réaliste des données séquentielles de hautes dimensions. Elle permet la découverte automatique de techniques de jeu, et peut être utilisée afin de créer de nouveaux modes de jeu, d'améliorer le système de classement, d'assister les commentateurs de "e-sport", ou de mieux analyser l'équipe adverse en amont, par exemple
It is extremely useful to exploit labeled datasets not only to learn models and perform predictive analytics but also to improve our understanding of a domain and its available targeted classes. The subgroup discovery task has been considered for more than two decades. It concerns the discovery of rules covering sets of objects having interesting properties, e.g., they characterize a given target class. Though many subgroup discovery algorithms have been proposed for both transactional and numerical data, discovering rules within labeled sequential data has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. In this thesis, we propose to apply bandit models and Monte Carlo Tree Search to explore the search space of possible rules using an exploration-exploitation trade-off, on different data types such as sequences of itemset or time series. For a given budget, they find a collection of top-k best rules in the search space w.r.t chosen quality measure. They require a light configuration and are independent from the quality measure used for pattern scoring. To the best of our knowledge, this is the first time that the Monte Carlo Tree Search framework has been exploited in a sequential data mining setting. We have conducted thorough and comprehensive evaluations of our algorithms on several datasets to illustrate their added-value, and we discuss their qualitative and quantitative results. To assess the added-value of one or our algorithms, we propose a use case of game analytics, more precisely Rocket League match analysis. Discovering interesting rules in sequences of actions performed by players and using them in a supervised classification model shows the efficiency and the relevance of our approach in the difficult and realistic context of high dimensional data. It supports the automatic discovery of skills and it can be used to create new game modes, to improve the ranking system, to help e-sport commentators, or to better analyse opponent teams, for example
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Reising, Justin. "Function Space Tensor Decomposition and its Application in Sports Analytics." Digital Commons @ East Tennessee State University, 2019. https://dc.etsu.edu/etd/3676.

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Recent advancements in sports information and technology systems have ushered in a new age of applications of both supervised and unsupervised analytical techniques in the sports domain. These automated systems capture large volumes of data points about competitors during live competition. As a result, multi-relational analyses are gaining popularity in the field of Sports Analytics. We review two case studies of dimensionality reduction with Principal Component Analysis and latent factor analysis with Non-Negative Matrix Factorization applied in sports. Also, we provide a review of a framework for extending these techniques for higher order data structures. The primary scope of this thesis is to further extend the concept of tensor decomposition through the use of function spaces. In doing so, we address the limitations of PCA to vector and matrix representations and the CP-Decomposition to tensor representations. Lastly, we provide an application in the context of professional stock car racing.
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Berky, Levente. "Vizualizace dat pro Ansible Automation Analytics." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445590.

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Tato práce se zaměřuje na vytvoření webové komponenty k vykreslení grafů ze strukturovanýho datovýho formátu (dále jen schéma) a vytvoření uživatelského rozhraní pro editaci schématu pro Ansible Automation Analytics. Práce zkoumá aktuální implementaci Ansible Automation Analytics a odpovídající API. Dále zkoumá vhodné knihovny pro vykreslování grafů a popisuje základy použitých technologií. Praktická část popisuje požadavky na komponentu a popisuje vývoj a implementaci pluginu. Dále práce popisuje proces testování a~plány budoucího vývoje pluginu.
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Zhang, Liangwei. "Big Data Analytics for Fault Detection and its Application in Maintenance." Doctoral thesis, Luleå tekniska universitet, Drift, underhåll och akustik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-60423.

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Big Data analytics has attracted intense interest recently for its attempt to extract information, knowledge and wisdom from Big Data. In industry, with the development of sensor technology and Information & Communication Technologies (ICT), reams of high-dimensional, streaming, and nonlinear data are being collected and curated to support decision-making. The detection of faults in these data is an important application in eMaintenance solutions, as it can facilitate maintenance decision-making. Early discovery of system faults may ensure the reliability and safety of industrial systems and reduce the risk of unplanned breakdowns. Complexities in the data, including high dimensionality, fast-flowing data streams, and high nonlinearity, impose stringent challenges on fault detection applications. From the data modelling perspective, high dimensionality may cause the notorious “curse of dimensionality” and lead to deterioration in the accuracy of fault detection algorithms. Fast-flowing data streams require algorithms to give real-time or near real-time responses upon the arrival of new samples. High nonlinearity requires fault detection approaches to have sufficiently expressive power and to avoid overfitting or underfitting problems. Most existing fault detection approaches work in relatively low-dimensional spaces. Theoretical studies on high-dimensional fault detection mainly focus on detecting anomalies on subspace projections. However, these models are either arbitrary in selecting subspaces or computationally intensive. To meet the requirements of fast-flowing data streams, several strategies have been proposed to adapt existing models to an online mode to make them applicable in stream data mining. But few studies have simultaneously tackled the challenges associated with high dimensionality and data streams. Existing nonlinear fault detection approaches cannot provide satisfactory performance in terms of smoothness, effectiveness, robustness and interpretability. New approaches are needed to address this issue. This research develops an Angle-based Subspace Anomaly Detection (ABSAD) approach to fault detection in high-dimensional data. The efficacy of the approach is demonstrated in analytical studies and numerical illustrations. Based on the sliding window strategy, the approach is extended to an online mode to detect faults in high-dimensional data streams. Experiments on synthetic datasets show the online extension can adapt to the time-varying behaviour of the monitored system and, hence, is applicable to dynamic fault detection. To deal with highly nonlinear data, the research proposes an Adaptive Kernel Density-based (Adaptive-KD) anomaly detection approach. Numerical illustrations show the approach’s superiority in terms of smoothness, effectiveness and robustness.
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Rezai, Arash. "Evaluation of development methods for mobile applications : Soundhailer’s site and iOS application." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-191124.

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To remain competitive and successful in today’s globalized market, companies need a strategy to ensure that they are constantly at the leading edge in terms of products and services. The implementation of a mobile application is one approach to fulfill this requirement. This report describes an overview of the topic, by introducing briefly today’s development tools for mobile application development and subsequently focusing on the Soundhailer application, as the application done by the author. The problem in focus is to find out whether a native or web-based application is preferred for an iOS application production strategy for a start-up company. Moreover, the report delivers an insight into a well-structured method that works good for setting up measuring points for a website, also Soundhailer’s, and the factual realization of a development tool for iOS development. This insight is based on a lot of help from a former student of the Royal Institute of Technology, who has had some previous experience within the area. To show prospective similarities and differences between theory and reality, the experiences are subsequently compared to the theoretical part. Finally, the results are critically discussed. Two versions of the application were developed, both a native version and a web-based version, and the results show that both native and web-based applications can be convenient solutions for companies to implement and use. The results also provide a foundation upon which others can build and better understand how an iOS application is used and developed.
För att förbli konkurrenskraftiga och framgångsrika i dagens globaliserade marknad, behöver företagen en strategi för att se till att de ständigt är i framkant när det gäller produkter och tjänster. Att framställa en mobilapplikation är ett av många sätt för att nå upp till detta krav. Denna rapport ger en överblick över ämnet genom att först gå igenom dagens utvecklingsverktyg för mobilapplikationer och därefter fokusera på företaget Soundhailers mobilapplikation, eftersom denne har utvecklats av undertecknad. Problemet i fokus består av att ta reda på om en hårdvarukodad eller webbaserad applikation är att föredra för produktionsstrategin av en iOSapplikation för ett start-up-företag. Dessutom ger rapporten en inblick i en välstrukturerad metod som fungerar bra för att inrätta mätpunkter för en webbplats, med fokus på Soundhailers webbplats, samt det faktiska genomförandet av ett utvecklingsverktyg för iOS-utveckling. Denna insikt bygger på en hel del hjälp från en före detta elev på Kungliga Tekniska Högskolan som har tidigare erfarenheter inom området. För att sedan visa potentiella likheter och skillnader mellan teori och verklighet jämförs erfarenheterna med den teoretiska delen. Slutligen diskuteras resultaten kritiskt. Två versioner av applikationen har utvecklats, både en hårdvarukodad version och en webbaserad version, och resultaten visar att både hårdvarukodade och webbaserade applikationer kan vara praktiska lösningar som företag kan implementera och använda sig av. Resultaten ger också en grund på vilken andra kan bygga vidare på samt en bättre förståelse för hur en iOSapplikation kan användas och utvecklas
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Raveneau, Vincent. "Interaction in Progressive Visual Analytics : an application to progressive sequential pattern mining." Thesis, Nantes, 2020. http://www.theses.fr/2020NANT4022.

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Le paradigme de Progressive Visual Analytics (PVA) a été proposé en réponse aux difficultés rencontrées par les Visual Analytics lors du traitement de données massives ou de l’utilisation d’algorithmes longs, par l’usage de résultats intermédiaires et par l’interaction entre humain et algorithmes en cours d’exécution. Nous nous intéressons d’abord à la notion d’“interaction”, mal définie en PVA, dans le but d’établir une vision structurée de ce qu’est l’interaction avec un algorithme en PVA. Nous nous intéressons ensuite à la conception et à l’implémentation d’un système et d’un algorithme progressif de fouille de motifs séquentiels, qui permettent d’explorer à la fois les motifs et les données sous-jacentes, en nous concentrant sur les interactions entre analyste et algorithme. Nos travaux ouvrent des perspectives concernant 1/ l’assistance de l’analyste dans ses interactions avec un algorithme dans un contexte de PVA; 2/ une exploration poussée des interactions en PVA; 3/ la création d’algorithmes nativement progressifs, ayant la progressivité et les interactions au cœur de leur conception
The Progressive Visual Analytics (PVA) paradigm has been proposed to alleviate difficulties of Visual Analytics when dealing with large datasets or time-consuming algorithms, by using intermediate results and interactions between the human and the running algorithm. Our work is twofold. First, by considering that the notion of “interaction” was not well defined for PVA, we focused on providing a structured vision of what interacting with an algorithm in PVA means. Second, we focused on the design and implementation of a progressive sequential pattern mining algorithm and system, allowing to explore both the patterns and the underlying data, with a focus on the analyst/algorithm interactions. The perspectives opened by our work deal with 1/ assisting analysts in their interactions with algorithm in PVA settings; 2/ further exploring interaction in PVA ; 3/ creating natively progressive algorithms, for which progressiveness and interaction are at the core of the design
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Abounia, Omran Behzad. "Application of Data Mining and Big Data Analytics in the Construction Industry." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu148069742849934.

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Books on the topic "Analytics Application"

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Ianchovichina, Elena. Inclusive growth analytics: Framework and application. [Washington, D.C: World Bank, 2009.

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Sugumaran, Vijayan, Zheng Xu, and Huiyu Zhou, eds. Application of Intelligent Systems in Multi-modal Information Analytics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51556-0.

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Sugumaran, Vijayan, Zheng Xu, and Huiyu Zhou, eds. Application of Intelligent Systems in Multi-modal Information Analytics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-51431-0.

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Sugumaran, Vijayan, Zheng Xu, Shankar P., and Huiyu Zhou, eds. Application of Intelligent Systems in Multi-modal Information Analytics. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-15740-1.

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Sugumaran, Vijayan, Zheng Xu, and Huiyu Zhou, eds. Application of Intelligent Systems in Multi-modal Information Analytics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74814-2.

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Sugumaran, Vijayan, Zheng Xu, and Huiyu Zhou, eds. Application of Intelligent Systems in Multi-modal Information Analytics. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-74811-1.

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Kapur, P. K., Gurinder Singh, Yury S. Klochkov, and Uday Kumar, eds. Decision Analytics Applications in Industry. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3643-4.

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Haber, Peter, Thomas Lampoltshammer, and Manfred Mayr, eds. Data Science – Analytics and Applications. Wiesbaden: Springer Fachmedien Wiesbaden, 2017. http://dx.doi.org/10.1007/978-3-658-19287-7.

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Haber, Peter, Thomas Lampoltshammer, Manfred Mayr, and Kathrin Plankensteiner, eds. Data Science – Analytics and Applications. Wiesbaden: Springer Fachmedien Wiesbaden, 2021. http://dx.doi.org/10.1007/978-3-658-32182-6.

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R, Shriram, and Mak Sharma, eds. Data Science Analytics and Applications. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8603-8.

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Book chapters on the topic "Analytics Application"

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Chatterjee, Ayan. "Application Analytics." In Building Apps for the Universal Windows Platform, 257–64. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2629-2_11.

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Agarwal, Reshu, Adarsh Dixit, and Shylaja Vinaykumar Karatangi. "Application of IoT in Water Supply Management." In Predictive Analytics, 199–212. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis Group, LLC, 2021. | Series: Advanced research in reliability and system assurance engineering: CRC Press, 2020. http://dx.doi.org/10.1201/9781003083177-12.

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Badiru, Adedeji B. "Application of DEJI Systems Model to Data Integration." In Data Analytics, 233–42. First edition. | Boca Raton, FL : CRC Press/Taylor & Francis: CRC Press, 2020. http://dx.doi.org/10.1201/9781003083146-8.

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Muhaisin, Mohammad Muhtady, and Taseef Rahman. "Application of Game Theory for Big Data Analytics." In Data Analytics, 199–210. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-8.

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Mohammad, Sheikh Suhail, Umar Maqbool, Aaquib Firdous, Tahleela Navid, Zahid Nazir Padder, and Shafqat Nabi Mughal. "Application of Demand-Side Management Techniques for Sustainable Energy." In Asset Analytics, 1–11. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3643-4_1.

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Gail, William B. "Application of Virtual Worlds to Environmental Security." In GeoSpatial Visual Analytics, 345–56. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-2899-0_27.

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Baru, Chaitanya, and Tilmann Rabl. "Application-Level Benchmarking of Big Data Systems." In Big Data Analytics, 189–99. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3628-3_10.

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Lee, Sharon X., Geoffrey McLachlan, and Saumyadipta Pyne. "Application of Mixture Models to Large Datasets." In Big Data Analytics, 57–74. New Delhi: Springer India, 2016. http://dx.doi.org/10.1007/978-81-322-3628-3_4.

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Jayasekara, Charitha Subhashi, Malka N. Halgamuge, Asma Noor, and Ather Saeed. "Analysis of Traffic Offenses in Transportation: Application of Big Data Analysis." In Data Analytics, 343–69. Boca Raton, FL : CRC Press/Taylor & Francis Group, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9780429446177-14.

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Guller, Mohammed. "Writing a Spark Application." In Big Data Analytics with Spark, 71–78. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0964-6_5.

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Conference papers on the topic "Analytics Application"

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Li, Ying, Ta-Hsin Li, Rong Liu, Jeaha Yang, and Juhnyoung Lee. "Application management services analytics." In 2013 IEEE International Conference on Service Operations and Logistics, and Informatics (SOLI). IEEE, 2013. http://dx.doi.org/10.1109/soli.2013.6611442.

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Jain, Prachi, Praveen Kumar, and Seema Rawat. "SmartTech: An Email Analytics Application." In 2018 International Conference on System Modeling & Advancement in Research Trends (SMART). IEEE, 2018. http://dx.doi.org/10.1109/sysmart.2018.8746959.

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Cordingly, Robert, Hanfei Yu, Varik Hoang, Zohreh Sadeghi, David Foster, David Perez, Rashad Hatchett, and Wes Lloyd. "The Serverless Application Analytics Framework." In Middleware '20: 21st International Middleware Conference. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3429880.3430103.

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Verma, Rakesh. "Security Analytics." In CODASPY '18: Eighth ACM Conference on Data and Application Security and Privacy. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3180445.3180456.

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Tamez, Giovanni, Danytza Castillo, Aaron Colmenero, Jorge A. Ayala, and Colleen Bailey. "Machine learning application to hydraulic fracturing." In Big Data: Learning, Analytics, and Applications, edited by Fauzia Ahmad. SPIE, 2019. http://dx.doi.org/10.1117/12.2518996.

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Baggag, Abdelkader, Abdulaziz Yousuf Al-Homaid, Tahar Zanouda, and Michael Aupetit. "Deep Learning for Traffic Analytics Application FIFA2022." In Qatar Foundation Annual Research Conference Proceedings. Hamad bin Khalifa University Press (HBKU Press), 2018. http://dx.doi.org/10.5339/qfarc.2018.ictpd544.

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"Analytics Driven Application Development for Healthcare Organizations." In International Conference on Health Informatics. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004705001350142.

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Chicaiza, Janneth, Ma Carmen Cabrera-Loayza, Rene Elizalde, and Nelson Piedra. "Application of data anonymization in Learning Analytics." In APPIS 2020: 3rd International Conference on Applications of Intelligent Systems. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3378184.3378229.

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Taluru, Danteswara Rao, and Rajendra Prasad Uppara Allabanda. "Application of Data Analytics in Gas Turbine Engines." In ASME 2019 Gas Turbine India Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gtindia2019-2557.

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Abstract:
Abstract In recent years, Aerospace technology has seen a paradigm shift towards Data analytics. Design, manufacturing, aftermarket and operations found importance of data analytics in reducing cost. Manufactures are developing Data Analytic based tools which help in optimizing their processes and reduce lead time. Engine, aircraft manufactures along with airliners are working together to improve customer experience. This paper, covers topics related to engine manufactures point of view. Engine manufactures can apply data analytics in concept initiation, concept optimization, preliminary, detailed and validation phases. Manufactures can optimize supply chain management using data analytics. Major gas turbine manufactures are persistent to enhance intelligence in aerospace product life cycle. Data analytics in aerospace engineering also helps in making predictions based upon descriptive patterns from huge data. Since, the aircraft industry is expecting a seven-fold increase in air traffic by 2050. The future demand of aircraft engine production would drive industries to adopt to big data in helping decision making and dynamic production capabilities. This paper helps in identifying different data analytics application in gas turbine product life cycle. Specifically in aero thermal discipline, which would help in increasing efficiency, optimizing design and result in reducing cost. This paper takes the reader through the application of gas turbine as a systematic and concise article. The future scope of the paper would include a test case explaining the application more in detail using data analytics.
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Saranya, G., G. Geetha, and M. Safa. "E-Antenatal assistance care using decision tree analytics and cluster analytics based supervised machine learning." In 2017 International Conference on IoT and Application (ICIOT). IEEE, 2017. http://dx.doi.org/10.1109/iciota.2017.8073617.

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Reports on the topic "Analytics Application"

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Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, and T. Li. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Report Two: Model Building at the Cell Level. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1431303.

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Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, T. Li, and A. Gel. Data Driven Smart Proxy for CFD Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part Three: Model Building at the Layer Level. Office of Scientific and Technical Information (OSTI), May 2018. http://dx.doi.org/10.2172/1463895.

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Ruch, Marc Lavi. Data Analytics for Nonproliferation Applications. Office of Scientific and Technical Information (OSTI), June 2019. http://dx.doi.org/10.2172/1529508.

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Kramer, Mitchell. Comparing Customer-Centric Analytic Applications. Boston, MA: Patricia Seybold Group, May 2002. http://dx.doi.org/10.1571/ca5-2-02cc.

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Kramer, Mitchell. Customer-Centric Analytic Application Feature Comparison Matrix. Boston, MA: Patricia Seybold Group, April 2002. http://dx.doi.org/10.1571/cm4-18-02cc.

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Kramer, Mitchell. What Are Customer-Centric Analytic Applications? Boston, MA: Patricia Seybold Group, December 2001. http://dx.doi.org/10.1571/fw12-21-01cc.

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Ansari, A., S. Mohaghegh, M. Shahnam, J. F. Dietiker, A. Takbiri Borujeni, and E. Fathi. Data Driven Smart Proxy for CFD: Application of Big Data Analytics & Machine Learning in Computational Fluid Dynamics, Part One: Proof of Concept; NETL-PUB-21574; NETL Technical Report Series; U.S. Department of Energy, National Energy Technology Laboratory: Morgantown, WV, 2017. Office of Scientific and Technical Information (OSTI), November 2017. http://dx.doi.org/10.2172/1417305.

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Kramer, Mitchell. Customer-Centric Analytic Applications within Teradata CRM. Boston, MA: Patricia Seybold Group, January 2002. http://dx.doi.org/10.1571/pr1-11-02cc.

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Kramer, Mitchell. PSGroup Bull's-Eye: Customer–Centric Analytic Applications. Boston, MA: Patricia Seybold Group, June 2002. http://dx.doi.org/10.1571/psgb6-20-02cc.

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Kramer, Mitchell. PSGroup Bull's-Eye: Customer–Centric Analytic Applications. Boston, MA: Patricia Seybold Group, July 2002. http://dx.doi.org/10.1571/psgb7-11-02cc.

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