Querying NoSQL-based crowdsourcing systems efficiently

In this paper, we provide a novel approach for effectively and efficiently support query processing tasks in novel NoSQL crowdsourcing systems. The idea of our method is to exploit the social knowledge available from reviews about products of any kind, freely provided by customers through specialized web sites. We thus define a NoSQL database system for large collections of product reviews, where queries can be expressed in terms of natural language sentences whose answers are modeled as lists of products ranked based on the relevance of reviews w.r.t. the natural language sentences. The best ranked products in the result list can be seen as the best hints for the user based on crowd opinions (the reviews). By exploiting the well-known IMDb dataset, which comprises more than 2 million reviews for more than 100,000 movies, we experimentally shows that our prototype obtains good performance in terms of execution time, demonstrating that our approach is feasible

NoXperanto: Crowdsourced Polyglot Persistence

This paper proposes NoXperanto , a novel crowdsourcing approach to address querying over data collections managed by polyglot persistence settings. The main contribution of NoXperanto is the ability to solve complex queries involving different data stores by exploiting queries from expert users (i.e. a crowd of database administrators, data engineers, domain experts, etc.), assuming that these users can submit meaningful queries. NoXperanto exploits the results of meaningful queries in order to facilitate the forthcoming query answering processes. In particular, queries results are used to: (i) help non-expert users in using the multi- database environment and (ii) improve performances of the multi-database environment, which not only uses disk and memory resources, but heavily rely on network bandwidth. NoXperanto employs a layer to keep track of the information produced by the crowd modeled as a Property Graph and managed in a Graph Database Management System (GDBMS)

Cat Tracks – Tracking Wildlife through Crowdsourcing using Firebase

Many mountain lions are killed in the state of California every year from roadkill. To reduce these numbers, it is important that a system be built to track where these mountain lions have been around. One such system could be built using the platform-as-a-service, Firebase. Firebase is a platform service that collects and manages data that comes in through a mobile application. For the development of cross-platform mobile applications, Flutter is used as a toolkit for developers for both iOS and Android. This entire system, Cat Tracks is proposed as a crowdsource platform to track wildlife, with the current focus on California mountain lions. By building such a system, researchers could use the data to save the lives of many mountain lions

Modeling Data Lake Metadata with a Data Vault

International audienceWith the rise of big data, business intelligence had to find solutions for managing even greater data volumes and variety than in data warehouses, which proved ill-adapted. Data lakes answer these needs from a storage point of view, but require managing adequate metadata to guarantee an efficient access to data. Starting from a multidimensional metadata model designed for an industrial heritage data lake presenting a lack of schema evolutivity, we propose in this paper to use ensemble modeling, and more precisely a data vault, to address this issue. To illustrate the feasibility of this approach, we instantiate our metadata conceptual model into relational and document-oriented logical and physical models, respectively. We also compare the physical models in terms of metadata storage and query response time

Towards the Development of a Framework for Socially Responsible Software by Analyzing Social Media Big Data on Cloud Through Ontological Engineering

AbstractA socially responsible internet is the need of the hour considering its huge potential and role in educating and transforming the society. Social computing is emerging as an important area as far as development of next generation web is concerned. With the proliferation of social networking applications, vast amount of data is available on cloud, which may be analyzed to gain useful insight into behavioral and linguistic patterns of different cultural and socio-economic groups further classified on the basis of gender and age etc. The idea is to come up with an appropriate framework for socially responsible software artifacts. These artifacts will monitor online social network data and analyze it from the perspective of socially responsible behavior based on ontological engineering concepts. Identification of socially responsible agents is such an example, though based on a different approach. More examples may be taken from literature dealing with microblog analytics, social semantic web, upper ontology for social web, and social-network-sourced big data analytics. In the present work, it is proposed to focus on analysis/monitoring of socially responsible behavior of social media big data and develop an upper level ontology as the framework/tool for such an analytics