Towards Efficient Path Query on Social Network with Hybrid RDF Management

The scalability and exibility of Resource Description Framework(RDF) model make it ideally suited for representing online social networks(OSN). One basic operation in OSN is to find chains of relations,such as k-Hop friends. Property path query in SPARQL can express this type of operation, but its implementation suffers from performance problem considering the ever growing data size and complexity of OSN.In this paper, we present a main memory/disk based hybrid RDF data management framework for efficient property path query. In this hybrid framework, we realize an efficient in-memory algebra operator for property path query using graph traversal, and estimate the cost of this operator to cooperate with existing cost-based optimization. Experiments on benchmark and real dataset demonstrated that our approach can achieve a good tradeoff between data load expense and online query performance

Decentralized Information Search

abstract: Our research focuses on finding answers through decentralized search, for complex, imprecise queries (such as "Which is the best hair salon nearby?") in situations where there is a spatiotemporal constraint (say answer needs to be found within 15 minutes) associated with the query. In general, human networks are good in answering imprecise queries. We try to use the social network of a person to answer his query. Our research aims at designing a framework that exploits the user's social network in order to maximize the answers for a given query. Exploiting an user's social network has several challenges. The major challenge is that the user's immediate social circle may not possess the answer for the given query, and hence the framework designed needs to carry out the query diffusion process across the network. The next challenge involves in finding the right set of seeds to pass the query to in the user's social circle. One other challenge is to incentivize people in the social network to respond to the query and thereby maximize the quality and quantity of replies. Our proposed framework is a mobile application where an individual can either respond to the query or forward it to his friends. We simulated the query diffusion process in three types of graphs: Small World, Random and Preferential Attachment. Given a type of network and a particular query, we carried out the query diffusion by selecting seeds based on attributes of the seed. The main attributes are Topic relevance, Replying or Forwarding probability and Time to Respond. We found that there is a considerable increase in the number of replies attained, even without saturating the user's network, if we adopt an optimal seed selection process. We found the output of the optimal algorithm to be satisfactory as the number of replies received at the interrogator's end was close to three times the number of neighbors an interrogator has. We addressed the challenge of incentivizing people to respond by associating a particular amount of points for each query asked, and awarding the same to people involved in answering the query. Thus, we aim to design a mobile application based on our proposed framework so that it helps in maximizing the replies for the interrogator's query by diffusing the query across his/her social network.Dissertation/ThesisM.S. Computer Science 201

Crowd Search: Generic Crowd Sourcing Systems Using Query Optimization

We think about the query optimization issue in Generic crowdsourcing system. Generic crowdsourcing is intended to conceal the complexities and calm the client the weight of managing the group. The client is just needed to present a SQL-like question and the framework assumes the liability of arranging the inquiry, creating the execution plan and assessing in the crowdsourcing commercial center. A given query can have numerous options execution arranges and the distinction in crowdsourcing expense between the best and the most exceedingly worst arranges may be a few requests of extent. In this manner, as in social database frameworks, query optimization is imperative to crowdsourcing frameworks that give revelatory question interfaces. In this paper, we propose CROWDOP, an expense based query advancement approach for explanatory crowdsourcing frameworks. CROWDOP considers both cost and latency in query advancement destinations and produces question arranges that give a decent harmony between the cost and latency. We create proficient calculations in the CROWDOP for upgrading three sorts of inquiries: selection queries join queries, and complex selection-join queries. Deco is a far reaching framework for noting decisive questions postured over put away social information together with information got on demand from the group. In this paper we assume Deco's cost based query streamlining agent, expanding on Deco's information model, query dialect, and query execution motor exhibited befor

GeoDjango-Framwork-based Population and Social Economic Information System For China

We have constructed China population and social economic information system based on open source geographic Web framework GeoDjango. By comparing different WebGIS system structures, implementation patterns, spatial database management patterns and mainstream popular open source APIs, open source WebGIS framework GeoDjango, MVT pattern, Python and MySQL database, client-side browser framework openLayer were implemented. The database system utilized a Top-Down design method — DATAID-D to establish distributed database, and construct a uniform encoding standard, through normalized processes of the statistic yearbook, we constructed population and social economic database. This system has basic functions of web information publishing, spatial data storage, distributed management and spatial query. Additionally, the system is able to meet multi-user access issues, and have functions of population and economy data visualization, thematic mapping, query and statistics, charting and output, et.al

Approximate Closest Community Search in Networks

Recently, there has been significant interest in the study of the community search problem in social and information networks: given one or more query nodes, find densely connected communities containing the query nodes. However, most existing studies do not address the "free rider" issue, that is, nodes far away from query nodes and irrelevant to them are included in the detected community. Some state-of-the-art models have attempted to address this issue, but not only are their formulated problems NP-hard, they do not admit any approximations without restrictive assumptions, which may not always hold in practice. In this paper, given an undirected graph G and a set of query nodes Q, we study community search using the k-truss based community model. We formulate our problem of finding a closest truss community (CTC), as finding a connected k-truss subgraph with the largest k that contains Q, and has the minimum diameter among such subgraphs. We prove this problem is NP-hard. Furthermore, it is NP-hard to approximate the problem within a factor $(2-\varepsilon)$, for any $\varepsilon >0$. However, we develop a greedy algorithmic framework, which first finds a CTC containing Q, and then iteratively removes the furthest nodes from Q, from the graph. The method achieves 2-approximation to the optimal solution. To further improve the efficiency, we make use of a compact truss index and develop efficient algorithms for k-truss identification and maintenance as nodes get eliminated. In addition, using bulk deletion optimization and local exploration strategies, we propose two more efficient algorithms. One of them trades some approximation quality for efficiency while the other is a very efficient heuristic. Extensive experiments on 6 real-world networks show the effectiveness and efficiency of our community model and search algorithms