AN PROFICIENT AND SCALABLE ORGANIZATION OF RESOURCE DESCRIPTION FRAMEWORK DATA IN THE CLOUD COMPUTING

A unusual technique construction to serve exquisite RDF dissolutions in sizable. Novel data arrangement strategies to co-locate semantically associated bits of data. Within this report, we recount RpCl, a decent and expandable dispersed RDF data supervision technique yet perplex. Unlike soon approaches, RpCl runs a corporeal evaluation of both proof and dummy instruction fronting separationing the science. The machinery keeps a sliding-window w tracking the modern good position for the load, counting associated data nearby in spite of joins that necessary ultimate performed and also the convicting edges. The structure combines join along pruning via RDF linear representation portrayal having a locality- stationed, even dissolutioning from the triples correct into a grid like, shared ratio organization. The Important Thing Index is a basic indicant in RpCl it utilizes a lexicovisual representationical tree to inspect each elect URI or accurate and select it a weird product key quality. Sharding such data applying understated techniques or separationing the chart accepting conventional min-cut conclusion gravitate very sloppy shared operations and also to a larger than volume of joins. Many RDF arrangements depose hash-subdivideing farther on appropriated selections, projections, and joins. Grid-Vine technique was by the whole of the first techniques act this poor massive decentralized RDF supervision. Within this script, we recount the construction of RpCl, its fundamental data organizations, better the new method we use to segregation and donate data. We assemble an considerable skim RpCl display our commodity is usually two orders of magnitude quicker than condition-of-the-art arrangements on test tasks at hands

Doctor of Philosophy

dissertationServing as a record of what happened during a scientific process, often computational, provenance has become an important piece of computing. The importance of archiving not only data and results but also the lineage of these entities has led to a variety of systems that capture provenance as well as models and schemas for this information. Despite significant work focused on obtaining and modeling provenance, there has been little work on managing and using this information. Using the provenance from past work, it is possible to mine common computational structure or determine differences between executions. Such information can be used to suggest possible completions for partial workflows, summarize a set of approaches, or extend past work in new directions. These applications require infrastructure to support efficient queries and accessible reuse. In order to support knowledge discovery and reuse from provenance information, the management of those data is important. One component of provenance is the specification of the computations; workflows provide structured abstractions of code and are commonly used for complex tasks. Using change-based provenance, it is possible to store large numbers of similar workflows compactly. This storage also allows efficient computation of differences between specifications. However, querying for specific structure across a large collection of workflows is difficult because comparing graphs depends on computing subgraph isomorphism which is NP-Complete. Graph indexing methods identify features that help distinguish graphs of a collection to filter results for a subgraph containment query and reduce the number of subgraph isomorphism computations. For provenance, this work extends these methods to work for more exploratory queries and collections with significant overlap. However, comparing workflow or provenance graphs may not require exact equality; a match between two graphs may allow paired nodes to be similar yet not equivalent. This work presents techniques to better correlate graphs to help summarize collections. Using this infrastructure, provenance can be reused so that users can learn from their own and others' history. Just as textual search has been augmented with suggested completions based on past or common queries, provenance can be used to suggest how computations can be completed or which steps might connect to a given subworkflow. In addition, provenance can help further science by accelerating publication and reuse. By incorporating provenance into publications, authors can more easily integrate their results, and readers can more easily verify and repeat results. However, reusing past computations requires maintaining stronger associations with any input data and underlying code as well as providing paths for migrating old work to new hardware or algorithms. This work presents a framework for maintaining data and code as well as supporting upgrades for workflow computations

Mechanisms of Controlled Sharing for Social Networking Users.

Social networking sites are attracting hundreds of millions of users to share information online. One critical task for all of these users is to decided the right audience with which to share. The decision about the audience can be at a coarse level (e.g., deciding to share with everyone, friends of friends, or friends), or at a fine level (e.g., deciding to share with only some of the friends). Performing such controlled sharing tasks can be tedious and error-prone to most users. An active social networking user can have hundreds of contacts. Therefore, it can be difficult to pick the right subset of them to share with. Also, a user can create a lot of content, and each piece of it can be shared to a different audience. In this dissertation, I perform an extensive study of the controlled sharing problem and propose and implement a series of novel tools that help social networking users better perform controlled sharing. I propose algorithms that automatically generate a recommended audience for both static profile items as well as real-time generated content. To help users better understand the recommendations, I propose a relationship explanation tool that helps users understand the relationship between a pair of friends. I perform extensive evaluations to demonstrate the efficiency and effectiveness of our tools. With our tools, social networking users can control sharing more accurately with less effort. Finally, I also study an existing controlled-sharing tool, namely the circle sharing tool for Google+. I perform extensive data analyses and examine the impact of friend groups sharing behaviors on the development of the social network.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/97999/1/ljfang_1.pd

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.European research projects - CoSpaces (IST-5-034245), CRESCENDO (FP7-234344) and MobiS (FP7-318452