View selection over knowledge graphs in triple stores

2021 ◽  
Vol 14 (13) ◽  
pp. 3281-3294
Author(s):  
Theofilos Mailis ◽  
Yannis Kotidis ◽  
Stamatis Christoforidis ◽  
Evgeny Kharlamov ◽  
Yannis Ioannidis
Keyword(s):  
Pattern Mining ◽  
Selection Process ◽  
Industrial Applications ◽  
Initial Time ◽  
View Selection ◽  
View Materialization ◽  
Subgraph Pattern ◽  
Knowledge Graphs ◽  
Selection Methodology ◽  
Submodular Set Function

Knowledge Graphs (KGs) are collections of interconnected and annotated entities that have become powerful assets for data integration, search enhancement, and other industrial applications. Knowledge Graphs such as DBPEDIA may contain billion of triple relations and are intensively queried with millions of queries per day. A prominent approach to enhance query answering on Knowledge Graph databases is View Materialization, ie., the materialization of an appropriate set of computations that will improve query performance. We study the problem of view materialization and propose a view selection methodology for processing query workloads with more than a million queries. Our approach heavily relies on subgraph pattern mining techniques that allow to create efficient summarizations of massive query workloads while also identifying the candidate views for materialization. In the core of our work is the correspondence between the view selection problem to that of Maximizing a Nondecreasing Submodular Set Function Subject to a Knapsack Constraint . The latter leads to a tractable view-selection process for native triple stores that allows a (1 - e ---1 )-approximation of the optimal selection of views. Our experimental evaluation shows that all the steps of the view-selection process are completed in a few minutes, while the corresponding rewritings accelerate 67.68% of the queries in the DBPEDIA query workload. Those queries are executed in 2.19% of their initial time on average.

Micro-Ribbons and Micro-Wires Silica Synthesis Using Bottom-Top Technique

2020 ◽  
Vol 1008 ◽  
pp. 33-38
Author(s):  
Marwa Nabil ◽  
Hussien A. Motaweh
Keyword(s):  
Selection Process ◽  
Chemical Properties ◽  
Industrial Applications ◽  
Preparation Process ◽  
Pure Silica ◽  
One Step ◽  
Materials Used ◽  
Sonication Technique ◽  
Silica Synthesis ◽  
Physical And Chemical

Silica is one of the most important materials used in many industries. The basic factor on which the selection process depends is the structural form, which is dependent on the various physical and chemical properties. One of the common methods in preparing pure silica is that it needs more than one stage to ensure the preparation process completion. The goal of this research is studying the nucleation technique (Bottom-top) for micro-wires and micro-ribbons silica synthesis. The silica nanoand microstructures are prepared using a duality (one step); a combination of alkali chemical etching process {potassium hydroxide (3 wt %) and n-propanol (30 Vol %)} and the ultra-sonication technique. In addition, the used materials in the preparation process are environmentally friendly materials that produce no harmful residues. The powder product is characterized using XRD, FTIR, Raman spectrum and SEM for determining the shape of architectures. The most significant factor of the nucleation mechanism is the sonication time of silica powder production during the dual technique. The product stages are as follows; silica nanoparticles (21-38 nm), nanoclusters silica (46 – 67 nm), micro-wires silica (1.17 – 6.29 μm), and micro-ribbons silica (19.4 – 54.1 μm). It's allowing for use in environmental applications (multiple wastewater purification, multiple uses in air filters, as well as many industrial applications).

Distributed Algorithm for High-Utility Subgraph Pattern Mining Over Big Data Platforms

2017 ◽  
Author(s):  
Alind Khare ◽  
Vikram Goyal ◽  
Srikanth Baride ◽  
Sushil K. Prasad ◽  
Michael McDermott ◽  
...  
Keyword(s):  
Big Data ◽  
Distributed Algorithm ◽  
Pattern Mining ◽  
Subgraph Pattern ◽  
High Utility

Shortening the selection process for sugarcane

1999 ◽  
Vol 35 (4) ◽  
pp. 391-405 ◽  
Author(s):  
L. D. MAMET ◽  
R. DOMAINGUE
Keyword(s):  
Selection Process ◽  
Sugar Industry ◽  
Cost Effective ◽  
Published Data ◽  
Industry Research ◽  
Stage 4 ◽  
The Usa ◽  
Improvement Programme ◽  
The Mean ◽  
Selection Methodology

There is a need to shorten the selection process for sugarcane (Saccharum hybrids) in Mauritius in order to improve the efficiency of the varietal improvement programme. On average six to seven ratoon crops are grown in Mauritius and selection for ratooning ability is of major importance. The current selection cycle lasts around 15 years and ratooning ability is tested on five occasions. Data (estimates of sucrose content, cane and sugar yields) from the Mauritius Sugar Industry Research Institute's selection trials planted in 1986–90, representing 85 trials (9680 genotypes) in Stage 3 (one-line stage) and 141 trials (2620 genotypes) in Stage 4 (two-line stage) were analysed. It was hypothesized that 1st ratoon (1R) data in Stage 3 and 2nd ratoon (2R) in Stage 4 were effectively redundant and that the cycle could be shortened by two years without loss of precision. Repeatability estimates, between plant cane (P) and the mean of P and 1R (P + 1R) in Stage 3, and between (P + 1R) and the mean of plant cane, 1st and 2nd ratoon (P + 1R + 2R) in Stage 4, were found to be positive and highly significant for all characters indicating that the extra ratoon data were unnecessary.Present and proposed selection scenarios were studied further using differential-selection methodology. The coincidence indices (CI) obtained with the two scenarios were extremely high (63–82% in Stage 3 and 91–96% in Stage 4) again indicating that the additional ratoon data were not cost effective. Realized gains from selection in Stage 4 based on (P + 1R + 2R) as opposed to (P + 1R) were shown to be small or even negative. The results concur extremely well with published data from Australia and the USA. It was therefore recommended that the cycle be reduced by two years and that the resources be more usefully allocated to test genotypes over more sites and more replicates.

scholarly journals Metastatic Site Prediction in Breast Cancer using Omics Knowledge Graph and Pattern Mining with Kirchhoff’s Law Traversal

2020 ◽  
Author(s):  
Alokkumar Jha ◽  
Yasar Khan ◽  
Ratnesh Sahay ◽  
Mathieu d’Aquin
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Pattern Mining ◽  
Knowledge Graph ◽  
Omics Data ◽  
Metastatic Site ◽  
Decision Network ◽  
Knowledge Graphs ◽  
Metastatic Sites ◽  
Kirchhoff's Law

AbstractPrediction of metastatic sites from the primary site of origin is a impugn task in breast cancer (BRCA). Multi-dimensionality of such metastatic sites - bone, lung, kidney, and brain, using large-scale multi-dimensional Poly-Omics (Transcriptomics, Proteomics and Metabolomics) data of various type, for example, CNV (Copy number variation), GE (Gene expression), DNA methylation, path-ways, and drugs with clinical associations makes classification of metastasis a multi-faceted challenge. In this paper, we have approached the above problem in three steps; 1) Applied Linked data and semantic web to build Poly-Omics data as knowledge graphs and termed them as cancer decision network; 2) Reduced the dimensionality of data using Graph Pattern Mining and explained gene rewiring in cancer decision network by first time using Kirchhoff’s law for knowledge or any graph traversal; 3) Established ruled based modeling to understand the essential -Omics data from poly-Omics for breast cancer progression 4) Predicted the disease’s metastatic site using Kirchhoff’s knowledge graphs as a hidden layer in the graph convolution neural network(GCNN). The features (genes) extracted by applying Kirchhoff’s law on knowledge graphs are used to predict disease relapse site with 91.9% AUC (Area Under Curve) and performed detailed evaluation against the state-of-the-art approaches. The novelty of our approach is in the creation of RDF knowledge graphs from the poly-omics, such as the drug, disease, target(gene/protein), pathways and application of Kirchhoff’s law on knowledge graph to and the first approach to predict metastatic site from the primary tumor. Further, we have applied the rule-based knowledge graph using graph convolution neural network for metastasis site prediction makes the even classification novel.

scholarly journals Greedy Selection of Materialized Views

2010 ◽  
pp. 47-58
Author(s):  
T.V. Vijay Kumar ◽  
Aloke Ghoshal
Keyword(s):  
Polynomial Time ◽  
Execution Time ◽  
Time Complexity ◽  
Materialized Views ◽  
View Selection ◽  
Multidimensional Lattice ◽  
View Materialization ◽  
Good Set ◽  
Selection Of

Greedy based approach for view selection at each step selects a beneficial view that fits within the space available for view materialization. Most of these approaches are focused around the HRU algorithm, which uses a multidimensional lattice framework to determine a good set of views to materialize. The HRU algorithm exhibits high run time complexity as the number of possible views is exponential with respect to the number of dimensions. The PGA algorithm provides a scalable solution to this problem by selecting views for materialization in polynomial time relative to the number of dimensions. This paper compares the HRU and the PGA algorithm. It was experimentally deduced that the PGA algorithm, in comparison with the HRU algorithm, achieves an improved execution time with lowered memory and CPU usages. The HRU algorithm has an edge over the PGA algorithm on the quality of the views selected for materialization.

scholarly journals Assessment and Selection of Technologies for the Sustainable Development of an R&D Center

2020 ◽  
Vol 12 (23) ◽  
pp. 10087
Author(s):  
Rafael Lizarralde ◽  
Jaione Ganzarain ◽  
Mikel Zubizarreta
Keyword(s):  
Decision Making ◽  
New Technologies ◽  
Selection Process ◽  
Multiple Criteria Decision Making ◽  
Industrial Applications ◽  
The Sustainable Development ◽  
Industrial Enterprises ◽  
Decision Making Model ◽  
Selection Of

The central role of R&D centers in the advancement of technology within industrial enterprises is undeniable and clearly affects their strategies, their competitiveness and their business sustainability. R&D centers assume responsibility for technology recognition, collection, acquisition, development and transition. Among their activities, the efficient choice of emerging technologies in the Technology Management Process is becoming a real challenge. In such heterogeneous scenarios, Multiple Criteria Decision Making (MCDM) models are commonly proposed as an appropriate decision-making approach. Multiple research works address the selection of particular technologies in industrial applications, but very few references can be found related to research institutions, and R&D centers in particular. Therefore, a decision-making model is provided in this study following the MIVES multi criteria method for the assessment of one or more technologies. The model is then applied to two case studies related to the selection process of new technologies at a Spanish R&D Center specialized in manufacturing.

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