Big SaaS: The Next Step Beyond Big Data

Software-as-a-Service (SaaS) is a model of cloud computing in which software functions are delivered to the users as services. The past few years have witnessed its global flourishing. In the foreseeable future, SaaS applications will integrate with the Internet of Things, Mobile Computing, Big Data, Wireless Sensor Networks, and many other computing and communication technologies to deliver customizable intelligent services to a vast population. This will give rise to an era of what we call Big SaaS systems of unprecedented complexity and scale. They will have huge numbers of tenants/users interrelated in complex ways. The code will be complex too and require Big Data but provide great value to the customer. With these benefits come great societal risks, however, and there are other drawbacks and challenges. For example, it is difficult to ensure the quality of data and metadata obtained from crowdsourcing and to maintain the integrity of conceptual model. Big SaaS applications will also need to evolve continuously. This paper will discuss how to address these challenges at all stages of the software lifecycle

"Living in Barcelona" Li-BCN workload 2010

Nowadays lots of Internet users are clients of web hosting companies, willing to offer their web services, store their content, or just publish their web sites on the network. This has made the hosting companies to use big data-centers or just the Cloud, in order to serve a web server, domain names, disk space and bandwidth to this great demand. In hosting companies, customers are often big companies or just private users or small business wanting to offer a web service or publish a website. Here we present and detail workloads from a set of different real web sites, of different owners and with different kind of content or offered web services. Some of them are personal or professional web-log sites, also small eCommerce sites, file storage/support sites, and information panel sites. The presented workload brings pieces of loads, that compared withPostprint (published version

Streaming Big Data Analysis for Real-Time Sentiment based Targeted Advertising

Big Data constituting from the information shared in the various social network sites have great relevance for research to be applied in diverse fields like marketing, politics, health or disaster management. Social network sites like Facebook and Twitter are now extensively used for conducting business, marketing products and services and collecting opinions and feedbacks regarding the same. Since data gathered from these sites regarding a product/brand are up-to-date and are mostly supplied voluntarily, it tends to be more realistic, massive and reflects the general public opinion. Its analysis on real time can lead to accurate insights and responding to the results sooner is undoubtedly advantageous than responding later.  In this paper, a cloud based system for real time targeted advertising based on tweet sentiment analysis is designed and implemented using the big data processing engine Apache Spark, utilizing its streaming library. Application is meant to promote cross selling and provide better customer support

Curación de contenidos desde bibliotecas: competencias, herramientas y aplicaciones

In the Age of Big Data we have at our disposal a great amount of information in different formats and from websites, repositories, blogs, databases, digitized files and documentary sources of all kinds, which presents a great challenge for the management and the analysis of the information that we apply day by day in our personal and labor scope. Given the difficulty of organizing and optimizing all this data flow, the content curator emerges as an intermediary of knowledge that discriminates against “informational waste” and facilitates quality on the web through search, selection and dissemination of content. Virtual environments permanently extend and complement the library’s services in time and space so it is necessary to think of activities and contents that are digital in order to capture the confidence of new users and reinforce their operational and informational capacities and competences through the new generation of technological applications that have emerged in recent years. From this premise, we analyze the origin and basic characteristics of this new professional profile, as well as the proposals and functionalities related

High-speed Train Control System Big Data Analysis Based on the Fuzzy RDF model and Uncertain Reasoning

China high-speed train control system is a combination of computer, communication and control. Its events are diverse, including sensor data stream, GPS signal, GSM-R transmission data, real-time video monitoring data, train control software data, etc. These data have the typical characteristics of big data. If these data are well applied, this will be of great help to operations, maintenance, safety, passenger services, etc. This paper presents an efficient analysis method based on the fuzzy RDF model and uncertain reasoning for high-speed train control system big data. We have used the method proposed in this paper to analyze the data of the high-speed train control system. The experiment results show that the method proposed in this paper has good efficiency and scalability for the analysis of big data with different structures, types and context sensitive from high-speed train control system