Influence analysis, derived from Social Network Analysis (SNA), is extremely useful in academic literature analytic. Different Academic Social Network Sites (ASNS) have been widely examined for ...influence analysis in terms of co-authorship and co-citation networks. The impact of other network-based features, such as followers and followings, provided by ASNS such as ResearchGate (RG) and Academia is yet to be anatomised. As proven in ingrained social theories, the followers and followings have significant impact in influence prorogation. This research aims at examining the same in one of the widely adopted ASNS, RG. The rendering process is developed to render real-time RG information, which is modelled into graph. Standard centrality measures are implemented to identify influential users from the constructed RG graph. Each centrality measure gives a list of top-k influential RG users. The results are compared with RGScore and Total Research Interest (TRI) to discover the most effective centrality measure. Betweenness and closeness centrality measures have shown the outperforming results compared with others. A procedure is established to discover influential RG users that are commonly present in all top-k centrality results to identify dominant skills, affiliations, departments and locations from the rendered data.
We address efficient processing of SPARQL queries over RDF datasets. The proposed techniques, incorporated into the gStore system, handle, in a uniform and scalable manner, SPARQL queries with ...wildcards and aggregate operators over dynamic RDF datasets. Our approach is graph based. We store RDF data as a large graph and also represent a SPARQL query as a query graph. Thus, the query answering problem is converted into a subgraph matching problem. To achieve efficient and scalable query processing, we develop an index, together with effective pruning rules and efficient search algorithms. We propose techniques that use this infrastructure to answer aggregation queries. We also propose an effective maintenance algorithm to handle online updates over RDF repositories. Extensive experiments confirm the efficiency and effectiveness of our solutions.
ABSTRACTWith recent technological advances, the efficient extraction and utilization of valuable information from large-scale data sources have become increasingly important. The development of ...knowledge graphs (KGs) based on logical relationships between data has garnered attention from various location-related services. To provide results that satisfy the diverse preferences of individuals, explicit attributes and implicit semantic context must be considered during the retrieval of places of interest (POIs). Most POI retrievals often involve not just examining detailed information about places but also specifying places for intended visits. Therefore, spatial knowledge regarding the surroundings of POIs, such as proximity and accessible routes, should be incorporated to support decision-making. In this study, we propose a comprehensive framework for constructing a KG for POI retrieval (PKG), which adeptly integrates the place attributes, semantic features, and spatial context of locations. The core objective of this framework is to acquire suitable data for facilitating POI retrieval that effectively considers diverse user preferences for places. After constructing a PKG of Orlando (FL, USA), we verified the practical applicability of the proposed framework by conducting 10 types of distinct POI retrieval queries catering to a range of user preferences. The graph queries returned a list of POIs that precisely aligned with the requirements of users on not only the explicit attributes of places but also the spatial and semantic features while providing detailed travel route information to these destinations. In conclusion, the PKG enabled POI retrieval that satisfied user preferences and diversified the retrieved places and the information provided. As the PKG offers flexibility in data integration without physical constraints, it can be expanded by incorporating information from various sources.
Given the fast pace of grid modernization, system states are changing more frequently and rapidly with the high penetration of renewable energy, responsive loads and power electronics interface. To ...properly monitor the dynamic change of system states and to further improve system operation reliability and robustness, a fast state estimator is required. This paper presents a graph computing-based state estimation. The feasibility of power system graph modeling is first demonstrated. The power system is naturally represented by a graph, in which its nodes serve as both storage units and logic units. Second, a graph computing technique for power system state estimation is presented. The system-level <inline-formula> <tex-math notation="LaTeX">{H} </tex-math></inline-formula> matrix and gain matrix are decomposed into locally formulated node-based matrices, and these node-based matrices are compressed to improve computational complexity. In addition, with graph topology analysis, the efficiency of the system-level gain matrix formulation and storage are further improved. The testing results of IEEE 14-bus system, IEEE 118-bus system, two European systems from MATPOWER, a provincial system in China, an MP-10790 system and an extended IEEE 118-bus*120 system demonstrate the high efficiency of the proposed approach without compromising the accuracy. Its advantages for high-performance computation are further illustrated by comparing it against a commercial EMS.
Large‐scale ontology management is one of the main issues when using ontology data practically. Although many approaches have been proposed in relational database management systems (RDBMSs) or ...object‐oriented DBMSs (OODBMSs) to develop large‐scale ontology management systems, they have several limitations because ontology data structures are intrinsically different from traditional data structures in RDBMSs or OODBMSs. In addition, users have difficulty using ontology data because many terminologies (ontology nodes) in large‐scale ontology data match with a given string keyword. Therefore, in this study, we propose a (graph database‐based ontology management system (GOMS) to efficiently manage large‐scale ontology data. GOMS uses a graph DBMS and provides new query templates to help users find key concepts or instances. Furthermore, to run queries with multiple joins and path conditions efficiently, we propose GOMS encoding as a filtering tool and develop hash‐based join processing algorithms in the graph DBMS. Finally, we experimentally show that GOMS can process various types of queries efficiently.
Graph databases have aroused a large interest in the last years due to their large scope of potential applications (e.g., social networks, biomedical networks, data stemming from the web). However, ...much published data suffer from quality problems, and graph data are no exception. In this paper, we investigate the issue of dealing with quality information in graph databases, at querying time. A framework is provided that makes it possible to introduce fuzzy quality preferences into graph pattern queries. This question is answered first from a theoretical point of view and then with an application to the Neo4j database management system by the extension of the cypher query language, for which implementation issues are discussed.
The work of criminal police in contemporary society is characterized by the proliferation of data and information to be processed, a more significant limitation of access to personal data, increased ...public monitoring, and higher expectations in the efficiency of identifying perpetrators. Also, all information collected during investigations is distributed through police systems separated by different levels and organization units, and often there are insufficient human and material resources. Resolving the perpetrator’s identity in those circumstances is a complex task, and police decision support systems must group all available evidence related to specific persons. For that purpose, this paper proposes a new approach to unconstrained pairwise clustering of face feature vectors extracted from the histogram of oriented gradients descriptor, named Truth-value clustering (TVC), based on non-axiomatic logic and graphs. The clustering approach was experimentally tested with six different face image databases. They were created to simulate unconstrained conditions like IARPA Janus Benchmark-B Face Dataset (IJB-B), IMDb-Wiki, Labeled Faces in the Wild, and YouTube Faces. The results of the proposed solution are compared with other state-of-the-art methods, showing that the approach gives, in summary, significantly better results. Application of the IJB-B protocol created for testing face clustering showed that the new approach gives better results by an average of 8.25% (σ=4.2). The main advantage over other methods is the possibility of utilizing mechanisms from non-axiomatic logic such as revision, which can then acquire new knowledge based on information from different nodes of the distributed environment consisting of various police information systems.
•Face clustering is base of a decision-supporting system for identity resolution.•The approach to pairwise face clustering is based on non-axiomatic logic.•Revision from non-axiomatic logic enables face clustering in a distributed environment.•The approach is tested on six datasets and compared with the state-of-the-art methods.
Graph Database Schema for Multimodal Transportation in Semarang Wirawan, Panji Wisnu; Er Riyanto, Djalal; Nugraheni, Dinar Mutiara Kusumo ...
Journal of information systems engineering and business intelligence,
10/2019, Letnik:
5, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Background: Semarang has broad area that cannot be covered entirely by single transportation mode. To reach a specific location, people often use more than one public transportation mode. Apart from ...Bus Rapid Transit, another exist namely angkot or city transportation. Multimodal traveler information is then required to help passenger searching for a route. Several studies of multimodal traveler information system has been conducted, however the data model for multimodal transportation did not conceived in detail.Objective: Proposes a database of multimodal transportation design using graph data model by taking Semarang as a case study.Method: We create our model in oriented entity-relationship diagram (O-ERD) and map this O-ERD to the graph database schema.Result: We develop our data model in graph database schema and we implement the model using Neo4J graph database for validation purpose. Our model consist of three graph node label namely Shelter, Angkot Stopper, and Closer Place. To validate our model, we execute a search query using the Cypher query to look for location with closer place to it.Conclusion: Our data model was successfully developed and implemented. Searching transportation route in the implementation of our model has been conducted using cypher query. It can successfully display all possible paths and routes. Our query can distinguish between one mode of transportation with another.Keywords: Graph database, Multimodal transportation, Neo4j, Cypher
Background:
Fast and computationally efficient strategies are required to explore genomic relationships within an increasingly large and diverse phage sequence space. Here, we present PhageClouds, a ...novel approach using a graph database of phage genomic sequences and their intergenomic distances to explore the phage genomic sequence space.
Methods:
A total of 640,000 phage genomic sequences were retrieved from a variety of databases and public virome assemblies. Intergenomic distances were calculated with dashing, an alignment-free method suitable for handling massive data sets. These data were used to build a Neo4j
®
graph database.
Results:
PhageClouds supported the search of related phages among all complete phage genomes from GenBank for a single query phage in just 10 s. Moreover, PhageClouds expanded the number of closely related phage sequences detected for both finished and draft phage genomes, in comparison with searches exclusively targeting phage entries from GenBank.
Conclusions:
PhageClouds is a novel resource that will facilitate the analysis of phage genomic sequences and the characterization of assembled phage genomes.
Biomechanical systems and applications in sport and rehabilitation regularly use different sensors. Use of sensors and smart equipment allows measurements of different physical quantities. As sensors ...can produce large amount of data, this has to be stored for post-processing and analysis. Every application has its own operation specific data flow and storage solution. Every application is different from another this is especially true in the relational database model. In this paper, we present a solution to this problem in a universal data model that can be used in any sensor-based application. We propose a cloud platform architecture that allows for the manipulation of sensor data and metadata. Signals and (meta) data that are associated with biomechanical systems are presented to underline the problem of universality; we present platform requirements and development of a cloud platform architecture. A shooting application usage with this platform is presented.
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