1,721,059 research outputs found
Smart RDF Data Storage in Graph Databases
No full textGraph Database Management Systems (GDBMs) provide an effective and efficient solution to data storage in current scenarios where data are more and more connected, graph models are widely used, and systems need to scale to large data sets. In particular, the conversion of the persistent layer of an application from a RDF to a graph data store can be convenient but it is usually an hard task for database administrators. In this paper we propose a methodology to convert a RDF data store to a graph database by exploiting the ontology and the constraints of the source. We provide experimental results that show the feasibility of our solution and the efficiency of query answering over the target database
Physical design for distributed RFID-based supply chain management
No full textIn consumer products market, supply chain management (SCM) is a complex and significant issue in the governance of organizations, people with their activities, technology, information and resources involved in transferring a product or service from a supplier to a final customer. To this aim, radio-frequency identification (RFID) is a promising wireless technology allowing to link an object with its “virtual counterpart”, i.e., its representation within information systems. In this context, a SCM system has to face a huge amount of RFID data, generated in the tracking of supply chain resources. In particular when RFID installations become larger and more physically distributed, efficient and scalable analysis of such data becomes a concern. Currently, state of the art approaches provide hard-coded solutions where the processing of RFID data occurs in a central location; as the amount and distribution of data grow, the workload requires significant consumption of resources, and quickly outpaces the capacity of a centralized processing server. In this paper, we consider the problem of distributing the RFID processing workload—possibly huge—proposing the physical design of a scalable and distributed system. Such system is built on top of a general framework for SCM, based on the first principles of linear algebra, in particular, on tensorial calculus. We consider challenges in instantiating such a system in large distributed settings, and design techniques for distributed real time query processing. Experimental results, using large traces, demonstrate the efficiency and scalability of our proposal with respect to competing approaches
Random Query Answering with the Crowd
No full textRandom data generators play an important role in computer science and engineering since they aim at simulating reality in IT systems. Software random data generators cannot be reliable enough for critical applications due to their intrinsic determinism, while hardware random data generators are difficult to integrate within applications and are not always affordable in all circumstances. We present an approach that makes use of entropic data sources to compute the random data generation task. In particular, our approach exploits the chaotic phenomena happening in the crowd. We extract these phenomena from social networks since they reflect the behavior of the crowd. We have implemented the approach in a database system, RandomDB, to show its efficiency and its flexibility over the competitor approaches. We used RandomDB by taking data from Twitter, Facebook and Flickr. The experiments show that these social networks are sources to generate reliable randomness and RandomDB a system that can be used for the task. Hopefully, our experience will drive the development of a series of applications that reuse the same data in several and different scenarios
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