Accessing Big Data in the Cloud Using Mobile Devices

2014 ◽  
pp. 444-470 ◽  
Author(s):  
Haoliang Wang ◽  
Wei Liu ◽  
Tolga Soyata
Keyword(s):  
Big Data ◽  
Mobile Devices ◽  
Response Times ◽  
Sensor Nodes ◽  
Data Generation ◽  
Ongoing Research ◽  
Mobile Access ◽  
Modern Computer ◽  
Processing Power ◽  
Require Response

The amount of data acquired, stored, and processed annually over the Internet has exceeded the processing capabilities of modern computer systems, including supercomputers with multiple-Petaflop processing power, giving rise to the term Big Data. Continuous research efforts to implement systems to cope with this insurmountable amount of data are underway. The authors introduce the ongoing research in three different facets: 1) in the Acquisition front, they introduce a concept that has come to the forefront in the past few years: Internet-of-Things (IoT), which will be one of the major sources for Big Data generation in the following decades. The authors provide a brief survey of IoT to understand the concept and the ongoing research in this field. 2) In the Cloud Storage and Processing front, they provide a survey of techniques to efficiently store the acquired Big Data in the cloud, index it, and get it ready for processing. While IoT relates primarily to sensor nodes and thin devices, the authors study this storage and processing aspect of Big Data within the framework of Cloud Computing. 3) In the Mobile Access front, they perform a survey of existing infrastructures to access the Big Data efficiently via mobile devices. This survey also includes intermediate devices, such as a Cloudlet, to accelerate the Big Data collection from IoT and access to Big Data for applications that require response times that are close to real-time.

Accessing Big Data in the Cloud Using Mobile Devices

2015 ◽  
pp. 222-248
Author(s):  
Haoliang Wang ◽  
Wei Liu ◽  
Tolga Soyata
Keyword(s):  
Big Data ◽  
Mobile Devices ◽  
Response Times ◽  
Sensor Nodes ◽  
Data Generation ◽  
Ongoing Research ◽  
Mobile Access ◽  
Modern Computer ◽  
Processing Power ◽  
Require Response

The amount of data acquired, stored, and processed annually over the Internet has exceeded the processing capabilities of modern computer systems, including supercomputers with multiple-Petaflop processing power, giving rise to the term Big Data. Continuous research efforts to implement systems to cope with this insurmountable amount of data are underway. The authors introduce the ongoing research in three different facets: 1) in the Acquisition front, they introduce a concept that has come to the forefront in the past few years: Internet-of-Things (IoT), which will be one of the major sources for Big Data generation in the following decades. The authors provide a brief survey of IoT to understand the concept and the ongoing research in this field. 2) In the Cloud Storage and Processing front, they provide a survey of techniques to efficiently store the acquired Big Data in the cloud, index it, and get it ready for processing. While IoT relates primarily to sensor nodes and thin devices, the authors study this storage and processing aspect of Big Data within the framework of Cloud Computing. 3) In the Mobile Access front, they perform a survey of existing infrastructures to access the Big Data efficiently via mobile devices. This survey also includes intermediate devices, such as a Cloudlet, to accelerate the Big Data collection from IoT and access to Big Data for applications that require response times that are close to real-time.

Big data quality framework: a holistic approach to continuous quality management

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Ikbal Taleb ◽  
Mohamed Adel Serhani ◽  
Chafik Bouhaddioui ◽  
Rachida Dssouli
Keyword(s):  
Big Data ◽  
Quality Management ◽  
Data Quality ◽  
Value Added ◽  
Holistic Approach ◽  
Research Area ◽  
Heterogeneous Data ◽  
Data Generation ◽  
Continuous Quality ◽  
Quality Profile

AbstractBig Data is an essential research area for governments, institutions, and private agencies to support their analytics decisions. Big Data refers to all about data, how it is collected, processed, and analyzed to generate value-added data-driven insights and decisions. Degradation in Data Quality may result in unpredictable consequences. In this case, confidence and worthiness in the data and its source are lost. In the Big Data context, data characteristics, such as volume, multi-heterogeneous data sources, and fast data generation, increase the risk of quality degradation and require efficient mechanisms to check data worthiness. However, ensuring Big Data Quality (BDQ) is a very costly and time-consuming process, since excessive computing resources are required. Maintaining Quality through the Big Data lifecycle requires quality profiling and verification before its processing decision. A BDQ Management Framework for enhancing the pre-processing activities while strengthening data control is proposed. The proposed framework uses a new concept called Big Data Quality Profile. This concept captures quality outline, requirements, attributes, dimensions, scores, and rules. Using Big Data profiling and sampling components of the framework, a faster and efficient data quality estimation is initiated before and after an intermediate pre-processing phase. The exploratory profiling component of the framework plays an initial role in quality profiling; it uses a set of predefined quality metrics to evaluate important data quality dimensions. It generates quality rules by applying various pre-processing activities and their related functions. These rules mainly aim at the Data Quality Profile and result in quality scores for the selected quality attributes. The framework implementation and dataflow management across various quality management processes have been discussed, further some ongoing work on framework evaluation and deployment to support quality evaluation decisions conclude the paper.

Big Data and Big Data Analytics for Improved Healthcare Service and Management

2020 ◽  
Vol 8 (1) ◽  
pp. 13-51
Author(s):  
Pijush Kanti Dutta Pramanik ◽  
Saurabh Pal ◽  
Moutan Mukhopadhyay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Services ◽  
Healthcare Sector ◽  
Future Market ◽  
Data Generation ◽  
Related Data ◽  
Healthcare Data ◽  
Different Types

Like other fields, the healthcare sector has also been greatly impacted by big data. A huge volume of healthcare data and other related data are being continually generated from diverse sources. Tapping and analysing these data, suitably, would open up new avenues and opportunities for healthcare services. In view of that, this paper aims to present a systematic overview of big data and big data analytics, applicable to modern-day healthcare. Acknowledging the massive upsurge in healthcare data generation, various ‘V's, specific to healthcare big data, are identified. Different types of data analytics, applicable to healthcare, are discussed. Along with presenting the technological backbone of healthcare big data and analytics, the advantages and challenges of healthcare big data are meticulously explained. A brief report on the present and future market of healthcare big data and analytics is also presented. Besides, several applications and use cases are discussed with sufficient details.

IoT-Based Big Data

2017 ◽  
Vol 13 (1) ◽  
pp. 28-47 ◽  
Author(s):  
M. Mazhar Rathore ◽  
Anand Paul ◽  
Awais Ahmad ◽  
Gwanggil Jeon
Keyword(s):  
Decision Making ◽  
Big Data ◽  
City Planning ◽  
Water System ◽  
Planning System ◽  
Data Generation ◽  
Next Generation ◽  
Smart Systems ◽  
Tier 1 ◽  
Iot Devices

Recently, a rapid growth in the population in urban regions demands the provision of services and infrastructure. These needs can be come up wit the use of Internet of Things (IoT) devices, such as sensors, actuators, smartphones and smart systems. This leans to building Smart City towards the next generation Super City planning. However, as thousands of IoT devices are interconnecting and communicating with each other over the Internet to establish smart systems, a huge amount of data, termed as Big Data, is being generated. It is a challenging task to integrate IoT services and to process Big Data in an efficient way when aimed at decision making for future Super City. Therefore, to meet such requirements, this paper presents an IoT-based system for next generation Super City planning using Big Data Analytics. Authors have proposed a complete system that includes various types of IoT-based smart systems like smart home, vehicular networking, weather and water system, smart parking, and surveillance objects, etc., for dada generation. An architecture is proposed that includes four tiers/layers i.e., 1) Bottom Tier-1, 2) Intermediate Tier-1, 3) Intermediate Tier 2, and 4) Top Tier that handle data generation and collections, communication, data administration and processing, and data interpretation, respectively. The system implementation model is presented from the generation and collection of data to the decision making. The proposed system is implemented using Hadoop ecosystem with MapReduce programming. The throughput and processing time results show that the proposed Super City planning system is more efficient and scalable.

Telecommunications- and Information Technology–Inspired Analyses: Review of an Intelligent Transportation Systems Experience

2017 ◽  
Vol 2658 (1) ◽  
pp. 44-55 ◽  
Author(s):  
Yupo Chan
Keyword(s):  
Information Technology ◽  
Big Data ◽  
Real Time ◽  
Traffic Management ◽  
Transportation Systems ◽  
Lessons Learned ◽  
Connected Vehicles ◽  
Ongoing Research ◽  
Real Time Information ◽  
Time Information

This paper reviews both the author’s experience with managing highway network traffic on a real-time basis and the ongoing research into harnessing the potential of telecommunications and information technology (IT). On the basis of the lessons learned, this paper speculates about how telecommunications and IT capabilities can respond to current and future developments in traffic management. Issues arising from disruptive telecommunications technologies include the ready availability of real-time information, the crowdsourcing of information, the challenges of big data, and the need for information quality. Issues arising from transportation technologies include autonomous vehicles and connected vehicles and new taxi-like car- and bikesharing. Illustrations are drawn from the following core functions of a traffic management center: ( a) detecting and resolving an incident (possibly through crowdsourcing), ( b) monitoring and forecasting traffic (possibly through connected vehicles serving as sensors), ( c) advising motorists about routing alternatives (possibly through real-time information), and ( d) configuring traffic control strategies and tactics (possibly though big data). The conclusion drawn is that agility is the key to success in an ever-evolving technological scene. The solid guiding principle remains innovative and rigorous analytical procedures that build on the state of the art in the field, including both hard and soft technologies. The biggest modeling and simulation challenge remains the unknown, including such rapidly emerging trends as the Internet of things and the smart city.

Automated Design of Realistic Contingencies for Big Data Generation

2021 ◽  
Author(s):  
Tetiana Bogodorova ◽  
Denis Osipov ◽  
Luigi Vanfretti
Keyword(s):  
Big Data ◽  
Automated Design ◽  
Data Generation

Knowledge Big Graph Fusing Ontology with Property Graph: A Case Study of Financial Ownership Network

2021 ◽  
Vol 48 (1) ◽  
pp. 55-71
Author(s):  
Xiao-Bo Tang ◽  
Wei-Gang Fu ◽  
Yan Liu
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Graph Model ◽  
Big Graph ◽  
Processing Power ◽  
Inference Algorithms ◽  
Data Environment ◽  
Fusion Framework ◽  
Contradictory Data

The scale of know­ledge is growing rapidly in the big data environment, and traditional know­ledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a know­ledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the know­ledge representation model is called know­ledge big graph. In addition, this paper applies the know­ledge big graph model to the ownership network in the China’s financial field and builds a financial ownership know­ledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership know­ledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the know­ledge big graph could provide technical reference for big data know­ledge organization and services.

User-Centric Social Interaction for Digital Cities

2014 ◽  
pp. 318-346 ◽  
Author(s):  
Kåre Synnes ◽  
Matthias Kranz ◽  
Juwel Rana ◽  
Olov Schelén
Keyword(s):  
Social Interaction ◽  
Internet Of Things ◽  
Information And Communication Technologies ◽  
Pervasive Computing ◽  
Social Computing ◽  
Open Data ◽  
Communication Technologies ◽  
Ongoing Research ◽  
Mobile Access ◽  
Information And Communication

Pervasive computing was envisioned by pioneers like Mark Weiser but has yet to become an everyday technology in our society. The recent advances regarding Internet of Things, social computing, and mobile access technologies converge to make pervasive computing truly ubiquitous. The key challenge is to make simple and robust solutions for normal users, which shifts the focus from complex platforms involving machine learning and artificial intelligence to more hands on construction of services that are tailored or personalized for individual users. This chapter discusses Internet of Things together with Social Computing as a basis for components that users in a “digital city” could utilize to make their daily life better, safer, etc. A novel environment for user-created services, such as social apps, is presented as a possible solution for this. The vision is that anyone could make a simple service based on Internet-enabled devices (Internet of Things) and encapsulated digital resources such as Open Data, which also can have social aspects embedded. This chapter also aims to identify trends, challenges, and recommendations in regard of Social Interaction for Digital Cities. This work will help expose future themes with high innovation and business potential based on a timeframe roughly 15 years ahead of now. The purpose is to create a common outlook on the future of Information and Communication Technologies (ICT) based on the extrapolation of current trends and ongoing research efforts.

The Compute Infrastructures for Big Data Analytics

2014 ◽  
pp. 74-109 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Sources ◽  
Steady Increase ◽  
Data Generation ◽  
Abnormal Growth ◽  
The World ◽  
Tremendous Amount ◽  
And Storage

The implications of the digitization process among a bevy of trends are definitely many and memorable. One is the abnormal growth in data generation, gathering, and storage due to a steady increase in the number of data sources, structures, scopes, sizes, and speeds. In this chapter, the author shows some of the impactful developments brewing in the IT space, how the tremendous amount of data getting produced and processed all over the world impacts the IT and business domains, how next-generation IT infrastructures are accordingly getting refactored, remedied, and readied for the impending big data-induced challenges, how likely the move of the big data analytics discipline towards fulfilling the digital universe requirements of extracting and extrapolating actionable insights for the knowledge-parched is, and finally, the establishment and sustenance of the dreamt smarter planet.

Accelerating Mobile-Cloud Computing

2015 ◽  
pp. 1933-1955
Author(s):  
Tolga Soyata ◽  
He Ba ◽  
Wendi Heinzelman ◽  
Minseok Kwon ◽  
Jiye Shi
Keyword(s):  
Cloud Computing ◽  
Mobile Devices ◽  
High Speed ◽  
Mobile Cloud Computing ◽  
Response Times ◽  
State Of The Art ◽  
The State ◽  
Mobile Cloud ◽  
Computational Resources ◽  
Traditional Approaches

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today's mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources.

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