A mobile cloud computing framework for execution of data as a service using cloudlet

The mobile device has become an essential utility tool for more effective computation, storage, and power, making it suitable for mobile cloud computing. The cloudlet is used as a connectivity establishment link between the mobile device and the cloud. The objective of this paper is to focus on mobile cloud computing facilitated with cloudlet-based computation. The latter possesses inter-cloudlet communication, which had been proposed within the mobile cloudletbased computing environment framework. The same had been further enhanced to scalable critical parameter yield of resources framework. Nevertheless, this was not taken to the criteria, which would impact the yield factor, in terms of availability. The present research endeavor aims to improve the algorithm by considering some more criterion and provides a new mobile cloud computing framework for data execution as a service using cloudlet. The outcome shows a positive result in the cloud- cloudlet based computation.

A Review Paper On Big Data Analytics in Cloud

The amount of data produced in the enterprise is increasing. Any industry will have to cope with exploding data volumes in the future, which will accelerate exponential data growth. It is critical to use a cost-effective, flexible approach for storing and analyzing this data. As a service to big data, the cloud will offer storage, platform, and software capabilities. Big data and cloud technologies are combining to make big data analytics in the cloud a viable choice. Data Analytics as a Service is another name for Cloud for Big Data Analytics. In this review paper we will get to know how big data analytics used cloud computing services for better performance or experience with their benefits, challenges and so on.

A Framework Of Big Data As Service Platform For Access Control & Privacy Protection Using Blockchain Network

Big Data As A Service Is Used In Today’s Scenario To Handle And Process The Big Amount Of Data Which Are Generated From Different Source Every Day. Since Data Is Stored On The Cloud Platform, The System Could Suffer A Failure And Give Attackers The Opportunity To Launch Various Categories Of Attacks.Manyresearcheshave Been Done In This Domain To Provide Security And Protection To The Data On Cloud. The Blockchain Technology Is A Secure, Distributed And Privacy-Preserving Decentralized Ledger Where The Transactions Are Flexible, Secure,Verifiable And Permanent Way.Here, The Transaction Data Is Encrypted Andkept In A Wrapped Block (I.E., Record) Which Are Spreadthrough The N/W In A Provable And Unabashedmode Across The Entire Network To Enhance Information Security And Data Privacy. In This Paperwe Have Proposed A Framework For An Access Control With Privacy Protection In Bdaas Based On Blockchain Technology. Here Blockchain Technology Is Used Only For Storing The Transaction Log Information Whenever Any Kind Of Event Log Occurred In System.

Integration Model between Heterogeneous Data Services in a Cloud

With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

From Reductionism to Reintegration: Solving society’s most pressing problems requires building bridges between data types across the life sciences

Decades of reductionist approaches in biology have achieved spectacular progress, but the proliferation of subdisciplines, each with its own technical and social practices regarding data, impedes the growth of the multidisciplinary and interdisciplinary approaches now needed to address pressing societal challenges. Data integration is key to a reintegrated biology able to address global issues such as climate change, biodiversity loss, and sustainable ecosystem management. We identify major challenges to data integration and present a vision for a “Data as a Service”-oriented architecture to promote reuse of data for discovery. The proposed architecture includes standards development, new tools and services, and strategies for career-development and sustainability.

Big Social Data as a Service (BSDaaS): A Service Composition Framework For Social Media Analysis

Social media provides an infrastructure where users can share their data at an unprecedented speed without worrying about storage and processing. Social media data has grown exponentially and now there is major interest in extracting any useful information from the social media data to apply in various domains. Currently, there are various tools available to analyze the large amounts of social media data. However, these tools do not consider the diversity of the social media data, and treat social media as a uniform data source with similar features. Thus, these tools lack the flexibility to dynamically process and analyze the social media data according to its diverse features. In this paper, we develop a `Big Social Data as a Service' (BSDaaS) composition framework that extracts the data from various social media platforms, and transforms it into useful information. The framework provides a quality model to capture the dynamic features of social media data. In addition, our framework dynamically assesses the quality features of the social media data and composes appropriate services required for various information analyses. We present a social media based sentiment analysis system as a motivating scenario and conduct experiments using real-world datasets to show the efficiency of our approach.