Indexing Method Research Articles

A new method for indexing continuous IoT data flows in metric space

The continuous production of heterogeneous internet of things (IoT) data created a new challenge, which is their storage and retrieve efficiently. In this work, a new method of indexing, called threshold distance (TD), is proposed in the fog‐cloud architecture. In this method, the fog layer is divided into two levels: a clustering level and an indexing level. In the clustering fog level, the first data flow is grouped into clusters of homogenous objects using the density‐based spatial clustering applications with noise (DBSCAN) algorithm. In the indexing fog level, objects in each cluster are indexed in separated generalized hyperplane‐trees (GHT). After the clustering of the next data flow, objects are inserted in existing GHT or new GHT are created according the comparison of the distances between centers of the next clusters and those of the first clusters (took as representatives of their corresponding indexes) with a threshold distance value. To test the efficiency of the TD method, the experimental results were compared with those of our second proposed method called creation of a new tree (CNT) in which, data of each cluster is indexed in a new GHT. The experimental results show that the efficiency of the TD method in GHT construction depend on the size of the data flow. For the parallel kNN search, the TD method proved its efficiency whatever the size of the data flow.

Risk assessment of interstate pipelines using a fuzzy-clustering approach

Interstate pipelines are the most efficient and feasible mean of transport for crude oil and gas within boarders. Assessing the risks of these pipelines is challenging despite the evolution of computational fuzzy inference systems (FIS). The computational intricacy increases with the dimensions of the system variables especially in the typical Takagi–Sugeno (T–S) fuzzy-model. Typically, the number of rules rises exponentially as the number of system variables increases and hence, it is unfeasible to specify the rules entirely for pipeline risk assessments. This work proposes the significance of indexing pipeline risk assessment approach that is integrated with subtractive clustering fuzzy logic to address the uncertainty of the real-world circumstances. Hypothetical data is used to setup the subtractive clustering fuzzy-model using the fundamental rules and scores of the pipeline risk assessment indexing method. An interstate crude-oil pipeline in Egypt is used as a case study to demonstrate the proposed approach.

Bridge-specific flood risk assessment of transport networks using GIS and remotely sensed data

A novel framework for the expedient assessment of flood risk to transportation networks focused on the response of the most critical and vulnerable infrastructure assets, the bridges, is developed, validated and applied. Building upon the recent French guidelines on scour risk (CEREMA, 2019), this paper delivers a thorough methodology, that incorporates three key, risk parameters: (i) the hydrodynamic loading, a hazard component of equal significance to scour, for the assessment of hazard; (ii) the correlation of select scour indicators with a new index relating to flow velocity, a primary measure of the adverse impacts of flow–structure interaction, enabling a more accurate and automated, assessment of bridge susceptibility to scour; (iii) the use of a new, comprehensive indicator, namely the Indicator of Flood Hazard Intensity (IFHI) which incorporates, in a simple yet efficient way, the key parameters controlling the severity of flood impact on bridges, namely flow velocity, floodwater height, flow obstruction, and sediment type. The framework is implemented for the analysis of flood risk in a case study area, considering an inventory of 117 bridges of diverse construction characteristics, which were affected by a major flood that impacted Greece in September 2020. The reliability of the method is validated against an extensive record of inspected and documented bridge damages. Regional scale analysis is facilitated by the adoption of the Multi-Criteria Decision-Making method for flood hazard indexing, considering geomorphological, meteorological, hydrological, and land use/cover data, based on the processing of remotely sensed imagery and openly available geospatial datasets in GIS.

Comparison of classical and developed indexing methods for assessing the groundwater suitability for irrigation

AbstractIntroductionSeveral water quality indices exist to evaluate irrigation water quality (IWQ), but they have some limitations. This study attempted to develop a new Overall Irrigation Water Quality Index (Overall IWQIndex) using a rational subindex that is flexible enough and updated to signify irrigation water suitability. It considered the maximum verified desirable and permissible value of parameters, including the hazard class, a modified rating system of parameters in each hazard class, and diversified water parameters that have affected the soil and plants.Materials and MethodsA total of 40 calcite‐type groundwater samples were analyzed to evaluate the IWQ using the proposed Overall IWQIndex, some IWQ parameters, and different diagram‐based statistical methods.ResultsThe study results were correlated with each other critically, and finally, the suitability for IWQ was determined. The results revealed that the samples of the postmonsoon and premonsoon felt under the ‘good’ and ‘excellent’ categories, respectively.ConclusionThe study developed an indexing model that is easy to compute, rationalize and deliver accurate information on the suitability of irrigation water.

Participatory Livelihood Vulnerability Assessment of the Forest Dwellers: A Study of Fifteen Tribes and Particularly Vulnerable Tribal Groups in the Eastern Indian Region

This study aims to undertake the Participatory Livelihood Vulnerability Assessment (PLVA) of forest dwellers in the Jharkhand and Odisha states of eastern Indian region. The study covers 15 tribes that included 8 Particularly Vulnerable Tribal Groups (PVTGs) and 7 other tribes from eight districts of Jharkhand and Odisha. The authors combined both participatory and survey-based approaches and used the simple indexing method for PLVA. They observed that landless families were among the most vulnerable. The forest dwellers have poor access to education and health. Both minor forest produce and social-welfare measures are important safety nets for the forest dwellers. There is, however, better access to banking, pukka houses, toilets and so on. Furthermore, attention is required to strengthen livelihood opportunities for the forest dwelling communities.

Assessment of wetland health dynamics: Comparing fuzzy- AHP and composite indexing methods in an urban agglomeration in east India

In Kolkata metropolitan area population growth, built-up land expansion, and infrastructure development shrinkages wetland area and degrades wetland health. Wetland health index was computed using composite-indexing (CI) and fuzzy-AHP (FA) models. Both CI and FA models performed well to assess wetland health where FA outperformed CI. The area under curve value ranges from 0.728 to 0.780 for CI and 0.745 to 0.855 for FA model. During the last two decades, the growth rate of built-up land was 1.86% per year and the shrinkage rate of wetland was 1.56% per year. The spatial coverage of excellent wetland area was decreased from 52.46% to 43.11% over last two decades, but the area under very poor wetlands have increased from 5.55% to 9.62%. Due to wetland health degradation, ecosystem service value was decreased by 23.50% over the last two decades. Wetland health has direct relationship with wetland size, which needs further research.

Improving Chinese Word Representation Using Four Corners Features

Intuitively, word representation for logographic languages like Chinese can be enhanced by its internal characteristics. Several research endeavors tried to learn Chinese word embeddings with characters, radicals, or subcharacters containing rich semantic information. In this paper, motivated by Four-Corner Method for Character Indexation, we extract features from four corners of characters with important morphological charactertics. Based on the features from four corners, we propose a model to utilize characters and four corner features of words to capture both semantic and morphological information. Moreover, we apply an attention scheme to integrate internal information dynamically, which includes two strategies to assign different weights for elements according to the word frequency. Experimental results on social news corpus and Chinese Wikipedia Dump show exploiting the four corner morphological features is crucial for capturing the meanings of Chinese words. Meanwhile, the results on word analogy, word similarity, and text classification tasks demonstrate that our approach obtains better results than state-of-the-art approaches.

Feasibility of a Generative Adversarial Network for Artifact Removal in Experimental Photoacoustic Imaging

Photoacoustic tomography (PAT) reconstruction is an expeditiously growing interest among biomedical researchers because of its possible transition from laboratory to clinical pre-eminence. Nonetheless, the PAT inverse problem is yet to achieve an optimal solution in rapid and precise reconstruction under practical constraints. Precisely, the sparse sampling problem and random noise are the main impediments to attaining accuracy but in support of rapid PAT reconstruction. The limitations are associated with acquiring undersampled artifacts that deteriorate the optimality of the reconstruction task. Therefore, the former achievements of fast image formation limit the modality for clinical settings. Delving into the problem, here we explore a deep learning-based generative adversarial network (GAN) to improve the image quality by denoising and removing these artifacts. The specially designed attributes and unique manner of optimizing the problem, such as incorporating the data set limitations and providing stable training performance, constitute the main motivation behind the employment of GAN. Moreover, exploitation of the U-net variant as a generator network offers robust performance in terms of quality and computational cost, which is further validated with the detailed quantitative and qualitative analysis. The quantitatively evaluated structured similarity indexing method=0.980 ± 0.043 and peak signal-to-noise ratio=31 ± 0.002 dB state that the proposed solution provides the high-resolution image at the output, even training with a low-quality data set.

Biological age for chronic kidney disease patients using index model.

The estimation of biological age (BA) is an important asymptomatic measure that can be used to understand the physical changes and the aging process of a living being. Factors that contribute towards profiling the human biological age can be diverse. Therefore, this study focuses on developing a BA model for patients with Chronic Kidney Disease (CKD). The procedure commences with the selection of significant biomarkers using a correlation test. Appropriate weighting is then assigned to each selected biomarker using the indexing method to produce a BA index. The BA index is matched to the age variation within the sample to acquire additional terms for the chronological age leading ultimately to the estimated BA. From a sample of 190 patients (133 trained data and 57 testing data) obtained from the University of Malaya Medical Centre (UMMC), Malaysia, the intensity of the BA is found to be between three to nine years from the chronological age. Visual observations further validate the high similarities between the training and testing data sets.

Assessment of surface water quality in a drinking water supply reservoir in Vietnam: a combination of different indicators

Eutrophication and water pollution have become serious aquatic environmental problems worldwide. In this study, four different water quality indexing methods, the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI), the revised Comprehensive Pollution Index (CPI), the Eutrophication Index (EI), and the Trophic State Index (TSI), were used to investigate water quality and trophic status in the Tri An Reservoir (TAR), Vietnam. Nineteen water variables were monitored bimonthly between February 2018 and December 2019 to calculate these four indices. Although different input data sets were used, all the indices showed similar results in terms of water quality and eutrophic state. The CCME WQI ranged from 32 to 68 (median 36–48), which indicated that the water quality was classified between poor and marginal and was insufficient for domestic purposes. Similarly, the water quality was ranked between slightly polluted and medium polluted based on the revised CPI index, with the median values ranging from 0.72 to 0.94. In the CCME WQI and the CPI, the major parameters that contributed to water pollution in the TAR were turbidity, dissolved oxygen (DO), total suspended solids (TSS), chemical and biological oxygen demand (BOD5), ammonium (NH4+), nitrite (NO2−), iron (Fe), lead (Pb), and coliforms. Regarding the trophic status of water, the EI ranged from 10 to 464, indicating that the reservoir was eutrophic, and the TSI values ranged from 53 to 89, indicating that the water quality was classified between light-eutrophic and hypereutrophic conditions. A temporal variation in water quality was found, with the highest levels of pollution recorded in June during the study period. Our results show that the combination of different water quality indices provides a comprehensive assessment of water quality in the TAR.

Indexing complex networks for fast attributed kNN queries

The k nearest neighbor (kNN) query is an essential graph data-management tool used for finding relevant data entities suited to a user-specified query node. Graph indexing methods have the potential to achieve a quick kNN search response and thus are promising approaches. However, they struggle to handle large-scale attributed complex networks. This is because constructing indices and querying kNN nodes in large-scale networks are computationally expensive, and they are not designed to handle node attributes included in the networks. In this paper, we propose a novel graph indexing algorithm, namely CT index, for fast kNN queries on large complex networks. To overcome the aforementioned limitations, our algorithm generates two types of indices based on the topological properties of complex networks. In addition, we further propose BAG index along with CT index so that our algorithm enables to explore kNN nodes based on the attribute similarity. Our extensive experiments on real-world graphs show that our algorithm achieves up to 18,074 times faster indexing and 146 times faster kNN query than other state-of-the-art methods.

Learned Semantic Index Structure Using Knowledge Graph Embedding and Density-Based Spatial Clustering Techniques

Recently, a pragmatic approach toward achieving semantic search has made significant progress with knowledge graph embedding (KGE). Although many standards, methods, and technologies are applicable to the linked open data (LOD) cloud, there are still several ongoing problems in this area. As LOD are modeled as resource description framework (RDF) graphs, we cannot directly adopt existing solutions from database management or information retrieval systems. This study addresses the issue of efficient LOD annotation organization, retrieval, and evaluation. We propose a hybrid strategy between the index and distributed approaches based on KGE to increase join query performance. Using a learned semantic index structure for semantic search, we can efficiently discover interlinked data distributed across multiple resources. Because this approach rapidly prunes numerous false hits, the performance of join query processing is remarkably improved. The performance of the proposed index structure is compared with some existing methods on real RDF datasets. As a result, the proposed indexing method outperforms existing methods due to its ability to prune a lot of unnecessary data scanned during semantic searching.

‘Arm’s length’ phraseology?

Abstract In the last decades, the study of phraseology within general and specialized lexicographic resources has been of interest to scholars. However, phraseology has not been studied in language for specific purposes (LSP) as much as in language for general purposes (LGP). Therefore, this study (i) offers an overview of the definitions regarding LSP phraseology, (ii) provides a series of linguistic analyses of specialized phraseological units (SPUs) extracted from a specialized bilingual dictionary, and (iii) draws a comparative line between LGP and LSP phraseology. To do so, 11,086 entries were extracted to build the analysis database. This study provides 1,054 morphosyntactic and 4,369 semantic patterns, a definition and a taxonomy of SPUs based on the data analysis and revision of LGP phraseology notions, and a hybrid lexicographic indexation method for SPUs. The contributions of this paper answer the question ‘what is a SPU?’; while highlighting similarities and differences with LGP phraseology.

Возрастная вариативность параметра оценочности ядерных единиц русского языкового сознания

The article is devoted to the problem of studying the age-related variability of the nuclear units of the Russian language consciousness. The aim of the work is to identify and describe the age-related variability of the evaluation parameter of nuclear words. The sources of the study were «Russian Regional Associative Dictionary-Thesaurus» and «Russian Associative Dictionary: Associative Reactions of Schoolchildren in Grades 1-11». The materials of these associative databases made it possible to collect empirical material for three age groups of subjects (7-11-year-olds, 12-16-year-olds, 17-21-year-olds). The method of continuous sampling from the associative fields of nuclear units-stimuli identified reactions that have evaluative connections with them. Quantitative analysis of the evaluation parameter of the studied units was carried out by the indexing method. Under the evaluation index of a nuclear unit, we mean the ratio of the number of associates having evaluative connections with a nuclear unit to the total number of reactions in its associative field. As a result of a quantitative analysis of the obtained data, we came to the following conclusions: the parameter of evaluation of the associative dominants of the Russian language consciousness has an age specificity, differing in numerical values and features of the dynamics. Positive dynamics of the evaluation parameter is registered in 9% of the studied nuclear units, while 1%t has negative dynamics. More than 60% of the studied nuclear words in the age variants of the core of the Russian language consciousness have multidirectional dynamics of the evaluation parameter.

A new index‐based hyper‐heuristic algorithm for global optimisation problems

In this research study, a new combination search algorithm, based on indexing its constituent processes, is proposed to solve global optimisation problems. As optimisation problems become more complex, especially real-world problems, the use of higher-performance algorithms has become essential. One of the techniques that lead to design of an algorithm with stronger strategies in exploration and exploitation, as well as a better balance between these two strategies, is the appropriate combination of parent algorithm processes. The proposed algorithm was developed using a new innovation with the help of the supply-demand-based optimisation and the Harris hawks optimisation algorithms processes as parent algorithms. In this algorithm, the local and global search sections of its parent algorithms, are separated, and then based on a new indexing method in each iteration, according to the current population indexing, a global search and a local search are selected from the processes of its parent algorithms, and then the current population is updated with two selected sections. The performance and effectiveness of the proposed algorithm in solving well-known standard benchmark problems and in solving real-world engineering problems have been tested and validated by statistical tools. The results of the research study show that the proposed algorithm can provide very effective results compared to other competing algorithms as well as its parent algorithms in many tests. The results show that the proper combination of optimisation algorithm processes can be used as a technique to design more powerful algorithms to solve global optimisation problems, especially complex real-world problems.

Temporally relevant parallel top-k spatial keyword search

New spatio-textual indexing methods are needed to support efficient search and update of the massive amounts of spatially referenced text being generated. Location based services using geo-tagged documents provide valuable ranked recommendations about nearby restaurants, services, sales, emergency events, and visitor attractions. Consequently, top-k spatial keyword search queries (TkSKQ) have received a lot of attention from the research community. Several spatio-textual indexes have been proposed to efficiently support TkSKQ. Some of these indexes support updates based on live document streams, but the ranking schemes employed by them do not simultaneously incorporate temporal relevance, textual similarity and spatial proximity. Moreover, existing approaches have limited or no capability to exploit parallelism with document ingestion and query execution. We present a parallel spatio-textual index, Pastri, to address the aforementioned issues. Pastri can be updated incrementally over real-time spatio-textual document streams. To support temporally relevant ranking of continuously generated document streams, we propose a dynamic ranking scheme. Our approach retrieves the top-k documents that are most temporally relevant at the time of a query execution. We implemented Pastri and we integrate it within a system with a persistent document store and several thread pools to exploit parallelism at various levels. Experimental evaluation involving real-world datasets and synthetic datasets (that we created) demonstrates that our system is able to sustain high document update throughput. Furthermore, Pastri's TkSKQ search performance is one to two orders of magnitude faster than other spatio-textual indexes.

Semi-global triangular centrality measure for identifying the influential spreaders from undirected complex networks

The influential spreaders play a significant role in maximizing or controlling any spreading process in a network. In the literature, many methods have been proposed to identify influential spreaders. In this article, we classify all the methods mainly into four categories, such as local centrality, global centrality, semi-global centrality and hybrid centrality. Among them, we have found semi-global centrality based methods have immense potential in identifying the influential spreaders from various types of network structures. However, we have observed that the existing semi-global centrality methods can identify the spreaders from the periphery of a network, where the nodes in the periphery are loosely coupled and the collective influence in the peripheral region of a spreading process will be nominal. We propose a new indexing method “semi-global triangular centrality”, which does not consider the best spreaders from the periphery. The proposed method maximizes the total collective influence of a spreading process by selecting the best spreaders from the dense part of a network. We have examined the performance of the proposed method using the Susceptible–Infected–Recovered epidemic model and applied to nine real-networks. The experimental result reveals that the proposed method performs better than the other centrality methods in terms of spreading dynamics.

Applied open-source Discrete Global Grid Systems

Abstract. Discrete Global Grid Systems (DGGS) are spatial reference systems that use a hierarchical tessellation of cells to partition and address the globe and provide alternative spatial data format and indexing methods as compared to traditional vector and raster spatial data. In order to effectively use DGGS, functional software needs to be available and data needs to be indexed into a DGGS. We compare the software APIs of the 5 main open-source DGGS implementations – Uber H3, Google S2, rHEALPix by Landcare Research New Zealand, RiskAware OpenEAGGR, and DGGRID by Southern Oregon University – and present exemplary workflows for converting spatial and vector and raster datasets into DGGS-indexed format. We summarize, that Uber H3 and Google S2 provide more mature software library functionalities and DGGRID provides excellent functionality to construct grids with desired geometric properties and to load point data but does not provide functions for traversal and navigation within a grid after its construction.

Validation of Alternative Left Atrial Indexation Methods in Obesity

Echocardiography guidelines recommend indexing left atrial (LA) size to body surface area (BSA) to account for body size.1 Indexation may, however, not be appropriate in patients with abnormal body stature as in obesity.2 Davis et al.2 recently showed that BSA indexation inadvertently affects the association between LA size and mortality in severely obese patients. Limitations to this study included the retrospective design and use of the ellipsoid method for quantifying LA size. We sought to validate their findings in a general population-based study with LA size quantified by the area-length method.

Optimal Allocation of Capacitors in Radial Distribution Networks Using Shannon's Entropy

Capacitor banks, when optimally placed in an electrical network improve power quality and reliability. For determining the capacitors’ optimal location, the prevailing index methods mainly focus on single criterion, e.g., either of voltage stability, loss reduction, system loadability, and neglect some of the equally critical quantities while proving the solution. For obtaining a pragmatically feasible techno-commercial solution, it is advantageous to consider multiple criteria. This article proposes a novel approach for optimal capacitor allocation, which allows to choose as many objective functions (criteria) as desired. In the proposed approach, initially, the load flow study is executed to obtain the network's quantities for an uncompensated network. Subsequently, optimal locations of capacitor banks are identified through the proposed indexing method based on the application of Shannon's Entropy. The capacitor banks’ optimal sizes are determined using the particle swarm optimization algorithm. The cost function is formulated as the minimization of sum of active power loss cost and the incurred annual capacitors cost subject to different operational constraints. The proposed approach is compared with that of the previously published methods by applying on radial distribution networks of different sizes (IEEE-12 bus, 34-bus and 108-bus) and the proposed approach is found to be superior.