Analytics Architecture Research Articles

Result Analysis of Big Data Analytical Architecture for Remote Sensing Application in Real time

Result Analysis of Big Data Analytical Architecture for Remote Sensing Application in Real time

Response to Turco and Zuckerman’s “Verstehen for Sociology”

Previous articleNext article No AccessResponse to Turco and Zuckerman’s “Verstehen for Sociology”Duncan WattsDuncan Watts Search for more articles by this author PDFPDF PLUSFull Text Add to favoritesDownload CitationTrack CitationsPermissionsReprints Share onFacebookTwitterLinkedInRedditEmail SectionsMoreDetailsFiguresReferencesCited by American Journal of Sociology Volume 122, Number 4January 2017 Article DOIhttps://doi.org/10.1086/690763 Views: 1212Total views on this site Citations: 1Citations are reported from Crossref © 2017 by The University of Chicago. All rights reserved.PDF download Crossref reports the following articles citing this article:Damon Mayrl and Nicholas Hoover Wilson What Do Historical Sociologists Do All Day? Analytic Architectures in Historical Sociology, American Journal of Sociology 125, no.55 (May 2020): 1345–1394.https://doi.org/10.1086/709017

A big data analytics architecture for cleaner manufacturing and maintenance processes of complex products

Cleaner production (CP) is considered as one of the most important means for manufacturing enterprises to achieve sustainable production and improve their sustainable competitive advantage. However, implementation of the CP strategy was facing barriers, such as the lack of complete data and valuable knowledge that can be employed to provide better support on decision-making of coordination and optimization on the product lifecycle management (PLM) and the whole CP process. Fortunately, with the wide use of smart sensing devices in PLM, a large amount of real-time and multi-source lifecycle big data can now be collected. To make better PLM and CP decisions based on these data, in this paper, an overall architecture of big data-based analytics for product lifecycle (BDA-PL) was proposed. It integrated big data analytics and service-driven patterns that helped to overcome the above-mentioned barriers. Under the architecture, the availability and accessibility of data and knowledge related to the product were achieved. Focusing on manufacturing and maintenance process of the product lifecycle, and the key technologies were developed to implement the big data analytics. The presented architecture was demonstrated by an application scenario, and some observations and findings were discussed in details. The results showed that the proposed architecture benefited customers, manufacturers, environment and even all stages of PLM, and effectively promoted the implementation of CP. In addition, the managerial implications of the proposed architecture for four departments were analyzed and discussed. The new CP strategy provided a theoretical and practical basis for the sustainable development of manufacturing enterprises.

An Efficient Multidimensional Big Data Fusion Approach in Machine-to-Machine Communication

Machine-to-Machine communication (M2M) is nowadays increasingly becoming a world-wide network of interconnected devices uniquely addressable, via standard communication protocols. The prevalence of M2M is bound to generate a massive volume of heterogeneous, multisource, dynamic, and sparse data, which leads a system towards major computational challenges, such as, analysis, aggregation, and storage. Moreover, a critical problem arises to extract the useful information in an efficient manner from the massive volume of data. Hence, to govern an adequate quality of the analysis, diverse and capacious data needs to be aggregated and fused. Therefore, it is imperative to enhance the computational efficiency for fusing and analyzing the massive volume of data. Therefore, to address these issues, this article proposes an efficient, multidimensional, big data analytical architecture based on the fusion model. The basic concept implicates the division of magnitudes (attributes), i.e., big datasets with complex magnitudes can be altered into smaller data subsets using five levels of the fusion model that can be easily processed by the Hadoop Processing Server, resulting in formalizing the problem of feature extraction applications using earth observatory system, social networking, or networking applications. Moreover, a four-layered network architecture is also proposed that fulfills the basic requirements of the analytical architecture. The feasibility and efficiency of the proposed algorithms used in the fusion model are implemented on Hadoop single-node setup on UBUNTU 14.04 LTS core i5 machine with 3.2GHz processor and 4GB memory. The results show that the proposed system architecture efficiently extracts various features (such as land and sea) from the massive volume of satellite data.

The Role of a Reference Synthetic Data Generator within the Field of Learning Analytics.

This paper details the anticipated impact of synthetic ‘big’ data on learning analytics (LA) infrastructures, with a particular focus on data governance, the acceleration of service development, and the benchmarking of predictive models. By reviewing two cases, one at sector wide level and the other at the institutional level - the Jisc learning analytics architecture and the UvAInform learning analytics project running at the University of Amsterdam - we explore the need for an on demand tool for generating a wide range of synthetic data. We argue that the application of synthetic data will not only accelerate the creation of complex and layered learning analytics infrastructure but also help to address the ethical and privacy risks involved during service development.

Big data analytic architecture for intruder detection in heterogeneous wireless sensor networks

Barrier coverage in Wireless Sensor Networks (WSNs) is an important research issue as intruder detection is the main purpose of deploying wireless sensors over a specified monitoring region. In WSNs, excessive volume and variety of sensor data are generated, which need to be analyzed for accurate measurement of the image in terms of width and resolution. In this paper, a three layered big data analytic architecture is designed to analyze the data generated during the construction of the barrier and detection of the intruder using camera sensors. Besides, a cloud layer is designed for storing the analyzed data to study the behavior of the intruder. In order to minimize the number of camera sensors for constructing the barrier, algorithms are designed to construct the single barrier with limited node mobility and the barrier path Quality of Sensing (QoS) is maintained with a minimum number of camera sensors. Simulation results show that our algorithms can construct 100% of the barrier with fewer number of camera sensors and average data processing time can be reduced by using parallel servers even if for larger size of data.

Big data architecture for construction waste analytics (CWA): A conceptual framework

In recent times, construction industry is enduring pressure to take drastic steps to minimise waste. Waste intelligence advocates retrospective measures to manage waste after it is produced. Existing waste intelligence based waste management software are fundamentally limited and cannot facilitate stakeholders in controlling wasteful activities. Paradoxically, despite a great amount of effort, the waste being produced by the construction industry is escalating. This undesirable situation motivates a radical change from waste intelligence to waste analytics (in which waste is propose to be tackle proactively right at design through sophisticated big data technologies). This paper highlight that waste minimisation at design (a.k.a. designing-out waste) is data-driven and computationally intensive challenge.The aim of this paper is to propose a Big Data architecture for construction waste analytics. To this end, existing literature on big data technologies is reviewed to identify the critical components of the proposed Big Data based waste analytics architecture. At the crux, graph-based components are used: in particular, a graph database (Neo4J) is adopted to store highly voluminous and diverse datasets. To complement, Spark, a highly resilient graph processing system, is employed. Provision for extensions through Building Information Modelling (BIM) are also considered for synergy and greater adoption. This symbiotic integration of technologies enables a vibrant environment for design exploration and optimisation to tackle construction waste.The main contribution of this paper is that it presents, to the best of our knowledge, the first Big Data based architecture for construction waste analytics. The architecture is validated for exploratory analytics of 200,000 waste disposal records from 900 completed projects. It is revealed that existing waste management software classify the bulk of construction waste as mixed waste, which exposes poor waste data management. The findings of this paper will be of interest, more generally to researchers, who are seeking to develop big data based simulation tools in similar non-trivial applications.

ID-Viewer: a visual analytics architecture for infectious diseases surveillance and response management in Pakistan

ObjectivesGlobally, disease surveillance systems are playing a significant role in outbreak detection and response management of Infectious Diseases (IDs). However, in developing countries like Pakistan, epidemic outbreaks are difficult to detect due to scarcity of public health data and absence of automated surveillance systems. Our research is intended to formulate an integrated service-oriented visual analytics architecture for ID surveillance, identify key constituents and set up a baseline for easy reproducibility of such systems in the future. Study designThis research focuses on development of ID-Viewer, which is a visual analytics decision support system for ID surveillance. It is a blend of intelligent approaches to make use of real-time streaming data from Emergency Departments (EDs) for early outbreak detection, health care resource allocation and epidemic response management. MethodsWe have developed a robust service-oriented visual analytics architecture for ID surveillance, which provides automated mechanisms for ID data acquisition, outbreak detection and epidemic response management. Classification of chief-complaints is accomplished using dynamic classification module, which employs neural networks and fuzzy-logic to categorize syndromes. Standard routines by Center for Disease Control (CDC), i.e. c1-c3 (c1-mild, c2-medium and c3-ultra), and spatial scan statistics are employed for detection of temporal and spatio-temporal disease outbreaks respectively. Prediction of imminent disease threats is accomplished using support vector regression for early warnings and response planning. Geographical visual analytics displays are developed that allow interactive visualization of syndromic clusters, monitoring disease spread patterns, and identification of spatio-temporal risk zones. ResultsWe analysed performance of surveillance framework using ID data for year 2011–2015. Dynamic syndromic classifier is able to classify chief-complaints to appropriate syndromes with high classification accuracy. Outbreak detection methods are able to detect the ID outbreaks in start of epidemic time zones. Prediction model is able to forecast dengue trend for 20 weeks ahead with nominal normalized root mean square error of 0.29. Interactive geo-spatiotemporal displays, i.e. heat-maps, and choropleth are shown in respective sections. ConclusionThe proposed framework will set a standard and provide necessary details for future implementation of such a system for resource-constrained regions. It will improve early outbreak detection attributable to natural and man-made biological threats, monitor spatio-temporal epidemic trends and provide assurance that an outbreak has, or has not occurred. Advanced analytics features will be beneficial in timely organization/formulation of health management policies, disease control activities and efficient health care resource allocation.

The Data Swarm

The traditional data warehouse model is no longer able to keep up with the evolution and changing requirements of the data analytic world. As we see the concept of a logical data warehouse gain momentum, there's a resulting need to drive a portion of the analytics closer to where the data is actually created and used. This paper uses the concept of swarm intelligence as a basis for simple, distributed analytics architecture to help address this need. It illustrates this with an example based on a chain of retail stores and demonstrates how this model could simplify the architecture and, at the same time, and increase data availability while decreasing cost.

IoT Big-Data Centred Knowledge Granule Analytic and Cluster Framework for BI Applications: A Case Base Analysis.

The current rapid growth of Internet of Things (IoT) in various commercial and non-commercial sectors has led to the deposition of large-scale IoT data, of which the time-critical analytic and clustering of knowledge granules represent highly thought-provoking application possibilities. The objective of the present work is to inspect the structural analysis and clustering of complex knowledge granules in an IoT big-data environment. In this work, we propose a knowledge granule analytic and clustering (KGAC) framework that explores and assembles knowledge granules from IoT big-data arrays for a business intelligence (BI) application. Our work implements neuro-fuzzy analytic architecture rather than a standard fuzzified approach to discover the complex knowledge granules. Furthermore, we implement an enhanced knowledge granule clustering (e-KGC) mechanism that is more elastic than previous techniques when assembling the tactical and explicit complex knowledge granules from IoT big-data arrays. The analysis and discussion presented here show that the proposed framework and mechanism can be implemented to extract knowledge granules from an IoT big-data array in such a way as to present knowledge of strategic value to executives and enable knowledge users to perform further BI actions.

Real-Time Big Data Analytical Architecture for Remote Sensing Application

The assets of remote senses digital world daily generate massive volume of real-time data (mainly referred to the term “Big Data”), where insight information has a potential significance if collected and aggregated effectively. In today’s era, there is a great deal added to real-time remote sensing Big Data than it seems at first, and extracting the useful information in an efficient manner leads a system toward a major computational challenges, such as to analyze, aggregate, and store, where data are remotely collected. Keeping in view the above mentioned factors, there is a need for designing a system architecture that welcomes both real-time, as well as offline data processing. Therefore, in this paper, we propose real-time Big Data analytical architecture for remote sensing satellite application. The proposed architecture comprises three main units, such as 1) remote sensing Big Data acquisition unit (RSDU); 2) data processing unit (DPU); and 3) data analysis decision unit (DADU). First, RSDU acquires data from the satellite and sends this data to the Base Station, where initial processing takes place. Second, DPU plays a vital role in architecture for efficient processing of real-time Big Data by providing filtration, load balancing, and parallel processing. Third, DADU is the upper layer unit of the proposed architecture, which is responsible for compilation, storage of the results, and generation of decision based on the results received from DPU. The proposed architecture has the capability of dividing, load balancing, and parallel processing of only useful data. Thus, it results in efficiently analyzing real-time remote sensing Big Data using earth observatory system. Furthermore, the proposed architecture has the capability of storing incoming raw data to perform offline analysis on largely stored dumps, when required. Finally, a detailed analysis of remotely sensed earth observatory Big Data for land and sea area are provided using Hadoop. In addition, various algorithms are proposed for each level of RSDU, DPU, and DADU to detect land as well as sea area to elaborate the working of an architecture.

Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods

The challenge of delivering personalized learning experiences is often increased by the size of classrooms and online learning communities. Serious Games (SGs) are increasingly recognized for their potential to improve education. However, the issues related to their development and their level of effectiveness can be seriously affected when brought too rapidly into growing online learning communities. Deeper insights into how the students are playing is needed to deliver a comprehensive and intelligent learning framework that facilitates better understanding of learners' knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper discusses current SOTA and aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model (ecosystem and architecture) discussed in this paper aims to highlight the key considerations that may advance the current state of learning analytics, adaptive learning and SGs, by leveraging SGs as an suitable medium for gathering data and performing adaptations.

Non-intrusive, out-of-band and out-of-the-box systems monitoring in the cloud

The dramatic proliferation of virtual machines (VMs) in datacenters and the highly-dynamic and transient nature of VM provisioning has revolutionized datacenter operations. However, the management of these environments is still carried out using re-purposed versions of traditional agents, originally developed for managing physical systems, or most recently via newer virtualization-aware alternatives that require guest cooperation and accessibility. We show that these existing approaches are a poor match for monitoring and managing (virtual) systems in the cloud due to their dependence on guest cooperation and operational health, and their growing lifecycle management overheads in the cloud. In this work, we first present Near Field Monitoring (NFM), our non-intrusive, out-of-band cloud monitoring and analytics approach that is designed based on cloud operation principles and to address the limitations of existing techniques. NFM decouples system execution from monitoring and analytics functions by pushing monitoring out of the targets systems' scope. By leveraging and extending VM introspection techniques, our framework provides simple, standard interfaces to monitor running systems in the cloud that require no guest cooperation or modification, and have minimal effect on guest execution. By decoupling monitoring and analytics from target system context, NFM provides ``always-on'' monitoring, even when the target system is unresponsive. NFM also works ``out-of-the-box'' for any cloud instance as it eliminates any need for installing and maintaining agents or hooks in the monitored systems. We describe the end-to-end implementation of our framework with two real-system prototypes based on two virtualization platforms. We discuss the new cloud analytics opportunities enabled by our decoupled execution, monitoring and analytics architecture. We present four applications that are built on top of our framework and show their use for across-time and across-system analytics.

A collaborative large spatio-temporal data visual analytics architecture for emergence response

The unconventional emergency, usually outbreaks more suddenly, and is diffused more quickly, but causes more secondary damage and derives more disaster than what it is usually expected. The data volume and urgency of emergency exceeds the capacity of current emergency management systems. In this paper, we propose a three-tier collaborative spatio-temporal visual analysis architecture to support emergency management. The prototype system, based on cloud computation environment, supports aggregation of massive unstructured and semi-structured data, integration of various computing model sand algorithms; collaborative visualization and visual analytics among users with a diversity of backgrounds. The distributed data in 100TB scale is integrated in a unified platform and shared with thousands of experts and government agencies by nearly 100 models. The users explore, visualize and analyse the big data and make a collaborative countermeasures to emergencies.

Os alvos da história da arquitetura: João Boltshauser e Sylvio de Vasconcellos

This article discusses the role of the history of architecture in architectural education, taking as a case study the trajectory of this discipline, from 1930 to 1964, at the School of Architecture of the Federal University of Minas Gerais, the first autonomous school of architecture in Brazil, which did not derive from the Academies of Fine Arts nor from polytechnic schools. In the early curricula, this perspective was represented by the discipline called “Analytic Architecture”, in which one studied in detail the architectural styles, which was taught by Joao Boltshauser, a professional with multiple roles as an architect, designer, philosopher, teacher and playwright. A very different perspective was the one adopted by Sylvio de Vasconcellos, who became the first professor of the institution responsible for the discipline called “Brazilian Architecture”. In his career three different fields intersect: architecture, heritage preservation and education, areas in which he worked with equal prominence. Key words: history, architecture, education.

An Analytical Framework for Web Information Filtering Techniques

Internet is a huge source of information. The growth of information and unstructured and semi structured nature of web information, cause some challenges for people to find their potential useful information for long-time needs. Hence, the implementation of automated tools selecting and evaluating information is necessary. Information filtering could be seen as a solution to this problem. It allows us to automatically filter out the unwanted content of the information. In this paper we first define the analytical architecture of the web information filtering system and second we suggest a systematic framework to classify web information filtering structures. We hope this proposed framework will lead to empirical and technical comparison of web information filtering structures and development of more efficient structures at future.

I‐SI: Scalable Architecture for Analyzing Latent Topical‐Level Information From Social Media Data

AbstractWe present a general visual analytics architecture that is designed and implemented to effectively analyze unstructured social media data on a large scale. Pipelined on a high‐performance cluster configuration, MPI processing, and interactive visual analytics interfaces, our architecture, I‐SI, closely integrates data‐driven analytical methods and user‐centered visual analytics. It creates a coherent analysis environment for identifying event structures, geographical distributions, and key indicators of emerging events. This environment supports monitoring, analyzing, and responding to latent information extracted from social media. We have applied the I‐SI architecture to collect social media data, analyze the data on a large scale and uncover the latent social phenomena. To demonstrate the efficacy and applicability of I‐SI, we describe several social media use cases in multiple domains that were evaluated by experts. The use cases demonstrate that I‐SI can benefit a range of users by constructing meaningful event structures and identifying precursors to critical events within a rich, evolving set of topics.

‘Who’ll Pay Reparations on My Soul?’1 Compensation, Social Control and Social Suffering

Contemporary debate about compensation for past wrongs turns on the assumption that state reparation benefits the victims of atrocity by acknowledging harm and ameliorating victim suffering. Indeed, much recent theoretical and practical work has concurred to establish reparation to victims of state crimes as a cornerstone of human rights. However, this article argues that reparation can also function to placate victim demands for criminal justice and to regulate the range of political and historical meanings with which the crimes of the past are endowed. This is most evident in transitional political contexts in which gestures of reparation are usually concomitant with the inauguration of new political orders, and formal investigations of past atrocity are conditioned by the balancing of the political demands of new and old regimes. This article argues that in such contexts, state reparation can work to control social suffering with the consequence that it sometimes intensifies rather than alleviates it. To evidence this claim, the article investigates the refusal of reparations by the victims towards whom it is addressed, with reference to Argentina’s Madres de Plaza de Mayo. This analysis of their refusal demonstrates how victim groups make important challenges to some of the core assumptions in the field, reveals internal inconsistencies within the analytical architecture of the scholarly and professional discourse, and indicates the ways in which reparations carry political, and not just palliative, significance.

Introduction to Linguistic Annotation and Text Analytics

Linguistic annotation and text analytics are active areas of research and development, with academic conferences and industry events such as the Linguistic Annotation Workshops and the annual Text Analytics Summits. This book provides a basic introduction to both fields, and aims to show that good linguistic annotations are the essential foundation for good text analytics. After briefly reviewing the basics of XML, with practical exercises illustrating in-line and stand-off annotations, a chapter is devoted to explaining the different levels of linguistic annotations. The reader is encouraged to create example annotations using the WordFreak linguistic annotation tool. The next chapter shows how annotations can be created automatically using statistical NLP tools, and compares two sets of tools, the OpenNLP and Stanford NLP tools. The second half of the book describes different annotation formats and gives practical examples of how to interchange annotations between different formats using XSLT transformations. The two main text analytics architectures, GATE and UIMA, are then described and compared, with practical exercises showing how to configure and customize them. The final chapter is an introduction to text analytics, describing the main applications and functions including named entity recognition, coreference resolution and information extraction, with practical examples using both open source and commercial tools. Copies of the example files, scripts, and stylesheets used in the book are available from the companion website, located at http://sites.morganclaypool.com/wilcock. Table of Contents: Working with XML / Linguistic Annotation / Using Statistical NLP Tools / Annotation Interchange / Annotation Architectures / Text Analytics

The study of applying ANP model to assess dispatching rules for wafer fabrication

Wafer fabrication is a capital-intensive high-tech sector with highly complex manufacturing processes. Therefore, most of the fabs resort to production dispatching as a means to enhancing production efficiency. The commonly seen methods of production dispatching at the present time are devised to meet single performance indicators. Few methods take into account multiple, or even conflicting performance indicators. Therefore, different production control managers adopt different criteria. Also, as performance indicators change with the variances of production lines and actual demands, it is necessary to clarify the rules of varying dispatching methods and their impacts on all the production performance indicators so that it is possible to explore an architecture for multiple-rule or multiple-target production dispatching in order to meet dynamic performance targets. This paper uses analytical network process (ANP) method to construct a dispatching model based on the characteristics of all the production facilities on-site (such as the utilization of bottleneck machines), in order to explore the relationship among various performance indicators and correlation between performance indicators and the dispatching rules. The aim of this paper is to analyze the production dispatching issues of wafer fabs in an effective and systematic approach, so as to provide an on-site dispatching analysis model that takes into consideration production characteristics and indicator adjustments. This paper finds that the most optimal dispatching method for ANP dispatch model is EDD dispatching method, followed by LS dispatching method. FIFO dispatching method yields the worst performance. The ANP dispatching assess model proposed in this paper can surely serves as an analytical architecture for decision makes to evaluate production dispatching models of multiple production indicators in the future.