The information system of low-voltage distribution network correctly records the topological structure of distribution network, which is the premise of fine management and safe operation of electric ...power grid. In the topology, the most important element is the household change relationship 1. At present, the low-voltage distribution network topology data in the information system is manually entered, so the correctness of the information can not be guaranteed. With the continuous expansion of the scale of power grid, the structure of low-voltage distribution network is complex, the amount of data of marketing measurement 2, GIS and other information systems is increasing rapidly, each information system operates independently, and the data circulation is poor, which makes it difficult to identify the topology data, so it is urgent to carry out topology verification. The method proposed in this paper effectively solves the problem of electric data verification, and carries out corresponding experiments and demonstrations on the results, with high accuracy.
Relational database users are switching to non-relational databases because non-relational databases are better able to handle dynamic data storage. One of the institutions that require dynamic data ...storage is Statistics Indonesia (BPS). Currently, data storage for census and survey activities at BPS is done using a relational database, although there are metadata changes in each activity. Accommodating metadata changes in each activity requires one database, which creates problems when retrieving some raw data. There is an opportunity for convenience if the data collected is stored in a non-relational database, one of which is a graph database. This research discusses the modeling of metadata and data from censuses and surveys at BPS using a graph database. Then we implement the Neo4j DBMS and compare the proposed model with the relational model in the Microsoft SQL Server DBMS. Then, a comparison of the features and characteristics of each DBMS is done, and finally, performance testing is done with Apache JMeter. Modeling has been able to handle dynamic data structure changes, but Neo4j's performance is still lagging behind Microsoft SQL Server.
Nowadays, numerous organisations of different dimensions and business sectors operate in highly challenging and dynamic environments, wherein the supporting information systems (IS) are becoming ...increasingly complex. In this context, assistive tools capable of tackling such complexity have the potential to aid users improving their performance and effectiveness, as well as to streamline businesses’ processes and promote entrepreneurial-level competitivity. Following this line of research, a web-based speech-to-term recognition approach is presented as a solution to endow IS with advanced capabilities for providing an easier (more natural) and straightforward interaction with baseline functionalities, by combining relevant techniques such as Voice Activity Detection (VAD), Automatic Speech Recognition (ASR), Natural Language Processing (NLP), and an Ontological Database (OD), mapping the IS’ functionalities and characteristics, is proposed. The developed interoperable system allows the conversion of speech to text - deriving into IS instructions - that is, in turn, submitted to on an ontological database wherein a term-based query is performed to elicit a set of available commands to be executed in the web context. These commands, fully mapped in the ontological database, are divided into three categories: a) navigation by menus/links, b) buttons interaction (e.g., submit forms) and c) completion of form fields. The proposed framework was experimentally tested in close to real conditions, resorting to an Enterprise Resource Planning (ERP) tool supplied by ERP Company.
Display omitted
•Proposed and implemented a reachability based pruning algorithm in e-healthcare.•Measure the efficiency of algorithm on Disease- Symptom and real datasets.•The comparison is done ...with ChainCoverPrune algorithm to our proposed algorithm.•Comparison done with recent work BFL+ as pruning algorithm.•Algorithms are tested for storage and access parametric measures on datasets.
We propose a Disease-Symptom graph database for our mobile-assisted e-healthcare application. A large Disease-Symptom graph is stored in the cloud and accessed using mobile devices over the Internet. Query and search are the fundamental operations of graph databases. However, while searching the Disease-Symptom graph for making preliminary diagnosis of diseases, queries become complex due to the complex structure of data and also queries are too hard to write and interpret. Moreover, it is not possible to access the graph frequently due to limited bandwidth of the network, transmission delay, and higher cost. Subgraph generation or pruning algorithm for appropriate inputs is one of the solutions to this problem. In this paper, we propose an efficient pruning algorithm by introducing a new approach to decompose the Disease-Symptom graph into a series of symptom trees (ST). All the Symptom trees are merged to build a pruned subgraph which is our requirement. We demonstrate the efficiency and effectiveness of our pruning algorithm both analytically and empirically and validate on Disease-Symptom graph database, as well as other real graph databases. Also a comparison is done with an efficient existing reachability based Chain Cover algorithm after modifying it ChainCoverPrune as pruning algorithm. These two algorithms are tested for storage and access parametric measures for querying the synthetic and real directed databases to show the efficiency of the proposed algorithm.
•Branches are screened by a heuristic scheme leveraging triangle structures of the power grid.•The proposed scheme reduces the contingency set with a low risk of missing critical branches.•A parallel ...graph database computing algorithm is designed for fast triangle detection.•A “Line Outage Severity Factor” is defined to accelerate the performance index calculation.•Techniques for avoiding redundant computations are also introduced.
In this paper, a triangle count-based branch contingency screening scheme is proposed for transmission power systems. Different triangle count algorithms are compared. A parallel triangle count algorithm is implemented in the graph database for speed-up. Then, a modified performance index is proposed. A structure-related constant called Line Outage Severity Factor (LOSF) is defined to avoid redundant computation when updating the screening results under different operating points. Numerical acceleration techniques are also leveraged to update the screening results when system topology changes. Experiments of N − 1 and N − 2 analysis on three testbed systems demonstrate that about 20% of contingency cases can be ruled out with relatively low risk in missing critical contingencies, which reduces the computational burdens for succeeding stages of the contingency analysis.
Three-dimensional dislocation networks control the mechanical properties such as strain hardening of crystals. Due to the complexity of dislocation networks and their temporal evolution, analysis ...tools are needed that fully resolve the dynamic processes of the intrinsic dislocation graph structure. We propose the use of a graph database for the analysis of three-dimensional dislocation networks obtained from discrete dislocation dynamics simulations. This makes it possible to extract (sub-)graphs and their features with relative ease. That allows for a more holistic view of the evolution of dislocation networks and for the extraction of homogenized graph features to be incorporated into continuum formulation. As an illustration, we describe the static and dynamic analysis of spatio-temporal dislocation graphs as well as graph feature analysis.
We propose to design vertex encoding for determinations of no-result edge queries that should not be executed. Edge query is one of the core operations in mainstream graph databases, which is to ...retrieve edges connecting two given vertices. Real-world graphs may be too large to be stored in memory and frequently accessing edge data on disk usually incurs much overhead. The average degree of real-world graph tends to be much less than the vertex number, and edges may not exist in most pairs of vertices. Efficiently avoiding no-result edge query executions will certainly improve the performance of graph database. In this article, we propose a new and important problem for determining no-result edge queries: vertex encoding for edge nonexistence determination (VEND, for short). We build a low dimensional vertex encoding for all vertices, and we can efficiently determine most vertex pairs that are connected by no edges just with their corresponding codes. The encoding can be efficiently adjusted when data updates happen. With VEND, we can utilize in-memory efficient operations to filter no-result disk accesses for edge query. We also design SIMD-oriented compression optimizations to further improve performance. Extensive experiments on real-world datasets confirm the effectiveness of our solution.
With an exponentially growing number of graphs from disparate repositories, there is a strong need to analyze a graph database containing an extensive collection of small- or medium-sized data graphs ...(e.g., chemical compounds). Although subgraph enumeration and subgraph mining have been proposed to bring insights into a graph database by a set of subgraph structures, they often end up with similar or homogenous topologies, which is undesirable in many graph applications. To address this limitation, we propose the Top-k Edge-Diversified Patterns Discovery problem to retrieve a set of subgraphs that cover the maximum number of edges in a database. To efficiently process such query, we present a generic and extensible framework called <inline-formula><tex-math notation="LaTeX">\textsc {Ted}^+</tex-math> <mml:math><mml:mrow><mml:mi>T</mml:mi><mml:mi>e</mml:mi><mml:msup><mml:mi>d</mml:mi><mml:mo>+</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href="huang-ieq3-3312566.gif"/> </inline-formula> which achieves a guaranteed approximation ratio to the optimal result. Three optimization strategies are further developed to improve the performance, and a lightweight version called TedLite is designed for even larger graph databases. Experimental studies on real-world datasets demonstrate the superiority of <inline-formula><tex-math notation="LaTeX">\textsc {Ted}^+</tex-math> <mml:math><mml:mrow><mml:mi>T</mml:mi><mml:mi>e</mml:mi><mml:msup><mml:mi>d</mml:mi><mml:mo>+</mml:mo></mml:msup></mml:mrow></mml:math><inline-graphic xlink:href="huang-ieq4-3312566.gif"/> </inline-formula> to traditional techniques.
•Graph modeling allows to retrieve information on the manufacturing industry data.•Transformation of tables coming from unstructured data improves recall and precision.•The semantic expansion of ...queries causes the appearance of less relevant results.•Considering the 'what' and 'about what' of a query generates accurate responses.
Manufacturing industry needs access to the data in order to realise its activities but also to generate new value-added knowledge. Nevertheless, it is confronted with a large and growing volume of heterogeneous data which limits its ability to exploit them optimally. Moreover, the data are distributed within different heterogeneous information systems, which limits the relationship exploration under the information retrieval process. Usually, the challenge is addressed by trying to manage and normalize the data structure in order to faster searching and exploiting them in a manufacturing context. For their part, the authors present i-Dataquest, an information retrieval system supported by (i) a graph-oriented model built from the structured and unstructured data of the company and (ii) a query system answering ‘what’ and ‘about what’ and (iii) generating three different results: a list of items, a list of property values and a list of sentences. The i-Dataquest prototype is built using Neo4J for the graph system generation, ConceptNet for lexical resource management and StandfordNLP for natural language processing. An evaluation of the prototype’s performance is conducted through a data set representing a drone manufacturer. The results show that the transformation of specific content such as tables in the graph and the semantic expansion of queries significantly improves the recall and precision measures. The results also suggest improving filtering less relevant results by considering particularly queries looking for a specific value.
PDF
