Indexing Scheme Research Articles

Trend Analysis of Technologies Supporting the Availability of Online Content: From Keyword-Based Search to the Semantic Web

Abstract The era of Web 1.0 implied the connection of web-based documents via links, which enabled search engines to scan for information and guarantee the search and availability of webpages. Web 2.0 represented the next evolutionary stage. Known as the collaborative web, the emphasis in this case was on the establishment of services and content by the community. Search options were complemented with labelling and frequently undesirable clickstream analysis coupled with push technology-supported information provision. The semantic web is a revolutionary development, which, in addition to processing information by humans, assures the readability of datasets by machines and facilitates communication between devices. In order to promote data and information processing by machines, the semantic web relies on a special ontology allocating the respective meaning to the given data along with relying on the global indexing and naming schemes of the web. Several ontologies emerged with differing basic guidelines while displaying compatibility to the RDF standard ranging from the more semantic description of bibliographical data in libraries to the description of information gained from social networks and human conversations. While Web 3.0 is often used interchangeably with the semantic web, the former one with its intelligent server function exceeds the semantic web. We have to ask ourselves, however, whether we can rely on the accuracy of the obtained data, and we must explore what progress have libraries – expected to increase reliability – made regarding the implementation of semantic data storage.

Zero-Shot Learning to Index on Semantic Trees for Scalable Image Retrieval.

In this study, we develop a new approach, called zero-shot learning to index on semantic trees (LTI-ST), for efficient image indexing and scalable image retrieval. Our method learns to model the inherent correlation structure between visual representations using a binary semantic tree from training images which can be effectively transferred to new test images from unknown classes. Based on predicted correlation structure, we construct an efficient indexing scheme for the whole test image set. Unlike existing image index methods, our proposed LTI-ST method has the following two unique characteristics. First, it does not need to analyze the test images in the query database to construct the index structure. Instead, it is directly predicted by a network learnt from the training set. This zero-shot capability is critical for flexible, distributed, and scalable implementation and deployment of the image indexing and retrieval services at large scales. Second, unlike the existing distance-based index methods, our index structure is learnt using the LTI-ST deep neural network with binary encoding and decoding on a hierarchical semantic tree. Our extensive experimental results on benchmark datasets and ablation studies demonstrate that the proposed LTI-ST method outperforms existing index methods by a large margin while providing the above new capabilities which are highly desirable in practice.

ST-Trie: A Novel Indexing Scheme for Efficiently Querying Heterogeneous, Spatiotemporal IoT Data

Recently, various environmental data, such as microdust pollution, temperature, humidity, etc., have been continuously collected by widely deployed Internet of Things (IoT) sensors. Although these data can provide great insight into developing sustainable application services, it is challenging to rapidly retrieve such data, due to their multidimensional properties and huge growth in volume over time. Existing indexing methods for efficiently locating those data expose several problems, such as high administrative cost, spatial overhead, and slow retrieval performance. To mitigate these problems, we propose a novel indexing scheme termed ST-Trie, for efficient retrieval over spatiotemporal IoT environment data. Given IoT sensor data with latitude, longitude, and time, the proposed scheme first converts the three-dimensional attributes to one-dimensional index keys. The scheme then builds a trie-based index, consisting of internal nodes inserted by the converted keys and leaf nodes containing the keys and pointers to actual IoT data. We leverage this index to process various types of queries. In our experiments with three real-world datasets, we show that the proposed ST-Trie index outperforms existing approaches by a substantial margin regarding response time. Furthermore, we show that the query processing performance via ST-Trie also scales very well with an increasing time interval. Finally, we demonstrate that when compressed, the ST-Trie index can significantly reduce its space overhead by approximately a factor of seven.

The impact of socioeconomic status on secondary prevention of the acute coronary syndrome

Abstract Background Although socioeconomic status (SES) has been reported to be associated with health inequities, there are limited studies exploring the association between SES and secondary prevention of acute coronary syndrome (ACS) in countries with universal health cover. Purpose The aim is to examine whether SES has an impact on the secondary prevention of ACS in Australia. Methods Australian SNAPSHOT ACS data (2012) and its 18-month follow-up data were linked to admissions data from 6 jurisdictions covering all states and territories, national death index and Medicare Pharmaceutical Benefits Scheme data covering up to 3 years post-discharge. The five SES groups (lowest in Group 1 and highest in Group 5) were derived from the Australian Bureau of Statistics Socio-Economic Indexes for Areas (SEIFA) using the residential postcode at baseline. Outcomes were cardiac rehabilitation (CR) participation and smoking rate at 18 months post discharge as well as the use of ≥3 of the 4 indicated medications, all-cause death and cardiovascular disease (CVD) rates by 36 months of discharge. Outcomes were compared between the groups using the multilevel logistic regression with covariates of SES (5 groups), sex, GRACE risk score (4 groups), ACS diagnosis (STEMI/NSTEMI/UA) and the jurisdictions where the admissions data were linked. Results Of 1655 patients with ACS (mean age 68±13.5 yrs, 65% were male), who were discharged from hospital alive and had linked data available, 353 (21%) were in SES Group 1 (lowest SES), 369 (22%) in Group 2, 382 (23%) in Group 3, 296 (18%) in Group 4 and 255 (15%) in Group 5 (highest SES). Baseline clinical characteristics were comparable across the five SES groups. At 18-month after discharge, 1014 (61%) patients were followed-up with comparable loss to follow-up in each group. After adjustment, fewer patients in the lower SES groups (Groups 1 and 2) had participated in CR than those in the highest SES group (Group 5) (OR (95% CI): 0.60 (0.36, 0.99) and 0.56 (0.35, 0.91), respectively). Moreover, the odds of smoking was greater in Group 3 than Group 5 (2.60 (1.15, 5.89)) but no trend was found across the groups. By 36 months of discharge after adjustment, there was no difference in the odds of using ≥3 out of 4 medications between the SES groups. Despite this, patients in Groups 1 and 2 were significantly more likely to die than those in the highest SES group (1.96 (1.19, 3.21) and 1.91 (1.19, 3.07), respectively). The odds of CVD readmission did not differ across SES groups. Conclusion This study suggests that patients with low SES were less likely to participate in CR programs and more likely to die than those with high SES. Smoking rates varied between patients with intermediate and high SES but no trend was found across the groups. Despite the universal health cover available, inequity between the SES groups still exist. Future research is needed to further explore strategies to help close the evidence-practice gaps. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Australian National Heart Foundation Postdoctoral Fellowship

Astro-TS3: Time-series Subimage Search Engine for archived astronomical data

Abstract Efficiently retrieving a subset of time series data in a specific sky region from large-scale astronomical observation data is important for some time-domain studies in astronomy, especially for research on long-term variable sources. Considering that the field of view of a telescope is generally larger than the effective sky region, the retrieval method at the full image level is relatively inefficient in terms of data transmission and storage. Therefore, this paper proposes a search engine dedicated to the efficient retrieval of time-series subimages in observed image data archives (Astro-TS3, Astronomical Time-Series Subimage Search Engine). An in situ and resilient indexing scheme is proposed, and it can build indexes for archived image data without any modification of the original files and support incremental updating with newly archived data. A subimage cutting tool is implemented to extract a part of the image according to the range specified by the user and generate a new Flexible Image Transport System (FITS) file for the extracted subimage. With Astro-TS3, users only need to submit requests including the ranges of the time and sky region to retrieve the required data and then download the time-series subimages after the preview. The evaluation of Astro-TS3 is performed using actual archived data from three Antarctic Survey Telescopes (AST3). The experimental data show that the time for the index establishment and incremental update increases approximately linearly relative to the number of data files. For retrieval, the response time of the data retrieval is approximately linearly related to the number and size of the subimages.

A Resilient Large-Scale Trajectory Index for Cloud-Based Moving Object Applications

The availability of location-aware devices generates tremendous volumes of moving object trajectories. The processing of these large-scale trajectories requires innovative techniques that are capable of adapting to changes in cloud systems to satisfy a wide range of applications and non-programmer end users. We introduce a Resilient Moving Object Index that is capable of balancing both spatial and object localities to maximize the overall performance in numerous environments. It is equipped with compulsory, discrete, and impact factor prediction models. The compulsory and discrete models are used to predict a locality pivot based on three fundamental aspects: computation resources, nature of the trajectories, and query types. The impact factor model is used to predict the influence of contrasting queries. Moreover, we provide a framework to extract efficient training sets and features without adding overhead to the index construction. We conduct an extensive experimental study to evaluate our approach. The evaluation includes two testbeds and covers spatial, temporal, spatio-temporal, continuous, aggregation, and retrieval queries. In most cases, the experiments show a significant performance improvement compared to various indexing schemes on a compact trajectory dataset as well as a sparse dataset. Most important, they demonstrate how our proposed index adapts to change in various environments.

Real duration and inflation duration: A cross country perspective on a multidimensional hedging strategy

In this study we consider two different duration measures: (i) real duration, which is a measure of a financial instrument (asset or liability) value sensitivity with respect to changes in the real interest rate, and (ii) expected-inflation duration, which is a measure of the instrument value elasticity with respect to changes in the expected rate of general price inflation. These two measures arise because the nominal interest rate is divisible into a real rate and the expected-inflation rate. Thus, when inflation is present, a duration measure depends on the source of the change in the interest rate. We empirically examine cross-country differences and show that in low (negative) nominal rate environments (France and Germany), nominal rates are less responsive to changes in inflation expectations that in a high nominal rate environment (Italy). We show that under-protection of cash flows against inflation may significantly lower the asset value with a sizeable expected-inflation duration. On the other hand, assets with an indexation scheme that over protects against inflation, will be significantly more expensive with nontrivial and negative elasticity with respect to the inflation rate. Finally, we demonstrate that the real and expected-inflation durtion can be utilized to simultaneously hedge the net worth of a firm against adverseimpacts of changes in the real interest rate and changes in the expected-inflation rate.

Practical interrogation scheme for the abrupt taper Mach-Zehnder interferometer refractive index sensor.

A practical interrogation scheme of a refractive index (RI) sensing system based on the abrupt fiber taper Mach-Zehnder interferometer sensor is designed, implemented, and demonstrated by experiment. The broadband light source and optical spectrum analyzer in the conventional design are replaced by two single-wavelength laser diodes modulated by a periodical square waveform and a simple photodetector (PD), which can significantly lower the cost and lead to easier integration. The photocurrent of the PD output signal is used as the indicator of the surrounding RI. Automatic data acquisition and processing is realized by LabVIEW programming. The experiment proves the feasibility of the new scheme and shows a high sensitivity (2371 mV/RIU) and high stability.

An indexing scheme and descriptor for 3D object retrieval based on local shape querying

A binary descriptor indexing scheme based on Hamming distance called the Hamming tree for local shape queries is presented. A new binary clutter resistant descriptor named Quick Intersection Count Change Image (QUICCI) is also introduced. This local shape descriptor is extremely small and fast to compare. Additionally, a novel distance function called Weighted Hamming applicable to QUICCI images is proposed for retrieval applications. The effectiveness of the indexing scheme and QUICCI is demonstrated on 828 million QUICCI images derived from the SHREC2017 dataset, while the clutter resistance of QUICCI is shown using the clutterbox experiment.

Sensitivity and sensitivity isotropy of an 8/4–4 parallel six-axis force sensor

PurposeSix-axis force sensors play an important role in civilian and military fields because of their multifunctionality. In the context of sensor structure design, sensitivity and sensitivity isotropy are often considered. This paper aims to study the possible relationship between the sensitivity/sensitivity isotropy and structural parameters of an 8/4–4 parallel six-axis force sensor. A comprehensive evaluation index and structural optimization design scheme are suggested in the end.Design/methodology/approachBased on the conditional number of the Jacobian matrix spectral norm, the sensitivity and sensitivity isotropy of the sensor are derived. Orthogonal experiments are used to determine the degree of primary and secondary factors that have a substantial effect on the sensor characteristics. The relationship between the performance indices and the structural parameters is analyzed by the performance atlas method. The comprehensive evaluation index lays the foundation for the structural optimization design of an 8/4–4 parallel six-axis force sensor.FindingsThe variation in each performance index of the sensor for each of the structural parameters is analyzed, and the structural parameters of the sensor with the desired performance indices can be easily selected from the performance atlases. A comprehensive performance evaluation index with a target value of 1 is proposed, and the overall influence of the structural parameters on the sensor performance index is investigated. A simulation example shows the feasibility of the proposed evaluation index.Originality/valueThe importance of each structural parameter of the 8/4–4 parallel six-axis force sensor is determined through orthogonal experiments in this paper. Relations among the structural parameters meeting the performance indices are derived and shown in the performance atlases. A comprehensive evaluation index is proposed to analyze the overall sensor performance.

Negative refraction in terbium at ultraviolet frequencies.

One of the key challenges in the development of negative index metamaterials is creating a sufficiently strong magnetic response in the material. Rare-earth ions can contain a strong optical magnetic response even in the ultraviolet region of the spectrum, which can be enhanced using magneto-electric cross-coupling. Using energies, transition strengths, and linewidths from atomic structure software, along with realistic inhomogeneous broadenings and densities in a solid, we simulate a negative index scheme using a terbium crystal at a wavelength of 282 nm.

Improving Historical Data Discovery in Weather Radar Image Data Sets Using Transfer Learning

Historical data discovery is a challenging task for any study in radar meteorology when the region of interest is recorded in weather observations. Weather radars exist in overlapping networks around the globe and are observing the atmospheric conditions around the clock. The observations are stored according to date, time, and location, as opposed to an indexing scheme based on the phenomena present in the scans themselves. Performing feature-based searches in these voluminous data sets is, at current, impossible. This research seeks to enable users seeking to study such phenomena as a mechanism for locating events of interest by leveraging recent progress from the fields of transfer learning and computer vision. Specifically, this work illustrates a methodology for performing image classification of precipitation regimes on colormapped weather radar scan images, as opposed to the raw data itself. This system reduces the data needed to perform this classification by two orders of magnitude, increasing throughput and democratizing usage of the deep learning tools for this task by allowing training and testing of the models on modest compute systems.

Identifying insufficient data coverage in databases with multiple relations

In today's data-driven world, it is critical that we use appropriate datasets for analysis and decision-making. Datasets could be biased because they reflect existing inequalities in the world, due to the data scientists' biased world view, or due to the data scientists' limited control over the data collection process. For these reasons, it is important to ensure adequate data coverage across different groups over the intersection of multiple attributes. Often, the dataset to be analyzed is obtained through complex joins and predicate combinations over multiple relational tables in a database. Due to the sheer data volume we often have to deal with, determining adequate coverage can require an unacceptably long execution time. In this paper, we provide an efficient approach for coverage analysis, given a set of attributes across multiple tables. To identify regions with insufficient coverage in the combinatorially large set of value combinations, we design an index scheme to avoid explicit table joins, achieve efficient memory usage, and support predicate combination at a high level of parallelism. We also propose P-WALK , a priority-based search algorithm, to traverse the lattice space. Since in practice, coverage assessment typically does not require precise COUNT aggregation results, we further present approximate methods to reduce computation time. Experimental evaluation using three real-world datasets shows the effectiveness, efficiency, and accuracy of proposed methods.

Study on the dual-Fano resonance generation and its potential for self-calibrated sensing.

Sensors based on Fano resonance (FR) have become a promising platform for various biological and chemical applications. However, most investigations on FR are limited to the generation of individual resonance. In this paper, based on the coupling between surface plasmon polariton (SPP) and two photonic waveguide modes, a dual-FR system is designed and analyzed. To explain the coupling mechanism, an extended temporal coupled-mode model is established to provide the physical insight. The spectral response obtained from the model matches well with the numerical one. Due to the decoupled nature of the FRs, a self-calibrated or dual-parameter sensing scheme for refractive index and temperature is proposed. The refractive index sensitivity up to 765 nm/RIU and temperature sensitivity up to 0.087 nm/°C are obtained by wavelength interrogation with figure-of-merit (FOM) up to 33260.9 RIU-1 and 3.78 °C-1 respectively. The proposed sensor provides great potential in fields of the multi-parameter sensing.

Distinctive author ranking using DEA indexing

The productivity and impact of a researcher can be measured by considering the total number of articles authored by him/her and corresponding citations. Several techniques exist to evaluate the cumulative impact of the author’s scholarly output & performance by comparing publications to citations. However, all of them fail to rank each author uniquely, resulting in the same index value assigned to two or more authors, although they have diverse citation patterns. In some indexing, beyond a certain number of citations of a particular article, the subsequent citations do not add any value to the overall indexing. In this paper, a new indexing scheme, based on data envelopment analysis, is proposed which ensures the unique ranking by identifying the different index values of the authors who have even a minimal difference in the citation pattern. Furthermore, the proposed scheme ensures that every citation will have impact without any ceiling. The index is applied to a consistent data set having publications data of the last 40 years in the field of Computer Science. The outcome, when compared with the existing metrics, confirms that the proposed index provides more effective results by ranking authors distinctively.

Design and Implementation of In-Memory Database System for Efficient Information Retrieval

The aim of design and implementation of this database is to minimize the retrieval time of various operations like Read, Search and Update then comparing the retrieval time on both the primary storage and secondary storage. The comparison will be done by using a suitable Data-Structure and an appropriate Indexing scheme which will help us to efficiently retrieve data. To perform this task selection of a suitable programming language is must. The result of comparison will be in the form of a time that is obtained by comparing the various database operations on both primary as well as secondary storage relies. IMDB’s shows great results when we deal with massive amount of data and we can retrieve data in short amount of time as compared to the secondary storage. Variations in amount of dataset are considered to show that with increase in the dataset the system responds to the queries more efficient. Difference between this database and the existing databases is that it will be using Data-Structure to segregate and sort the present data first thereafter using indexing scheme in searching data and applying different operations which will result in faster responses. Selection of a suitable data-set and performing operations on a massive amount of data of that data-set and comparing the retrieval time.

DAPR-tree: a distributed spatial data indexing scheme with data access patterns to support Digital Earth initiatives

ABSTRACT This paper proposes a novel data indexing scheme, the distributed access pattern R-tree (DAPR-tree), for spatial data retrieval in a distributed computing environment. As compared to traditional distributed indexing schemes, the DAPR-tree introduces the data access patterns during the indexing utilization stage so that a more balanced indexing structure can be provided for spatial applications (e.g. Digital Earth data warehouse). In this new indexing scheme, (a) an indexing penalty matrix is proposed by considering the balance of data number, topology and access load between different indexing nodes; (b) an ‘access possibility’ element is integrated to a classic ‘Master-Client’ structure for a distributed indexing environment; and (c) indexing algorithm for the DAPR-tree is provided for index implementations. By using a duplication of official GEOSS Clearinghouse system as a case study, the DAPR-tree was evaluated in a number of scenarios. The results show that our indexing schemes generally outperform (around 9%) traditional distributed indices with the utilization of data access patterns. Finally, we discuss the applicability of the DARP-tree and document DARP-tree shortcomings to encourage researchers pursuing related topics in Big Data indexing for Digital Earth and other geospatial initiatives.

Application of generalized Choquet fuzzy integral method in the sustainability rating of green buildings based on the BSAM scheme

The need to reduce the impact of building projects on the sustainability of the built environment and improve the use of resources necessitated several interventions such as the development of methods to assess building impacts and improve the sustainability performance of buildings. Using the BSAM scheme – a green building rating system developed specifically for the sub-Saharan region of Africa, the generalized Choquet fuzzy integral method was employed to determine the importance weights of the sustainability assessment criteria. Data collected from industry experts form the base inputs for the impact of the various sustainability criteria based on the local variations. Consequently, the building sustainability evaluation index and grading scheme were developed to measure and evaluate the sustainability performance of buildings. The developed sustainability rating model was validated in four real-world case studies to demonstrate its usefulness and robustness in practice. The findings revealed that the conventional approach of aggregation of points used by the existing green rating tools is less effective in dealing with criteria that have interactive characteristics. Also, assessment criteria such as sustainable construction practices, transportation, and energy have a significant impact on the sustainability of buildings. The study provides substantial contributions to the existing body of knowledge about green building assessment systems for built environment stakeholders, both from the theoretical and practical perspectives.

Pushing the Scalability of RDF Engines on IoT Edge Devices.

Semantic interoperability for the Internet of Things (IoT) is enabled by standards and technologies from the Semantic Web. As recent research suggests a move towards decentralised IoT architectures, we have investigated the scalability and robustness of RDF (Resource Description Framework)engines that can be embedded throughout the architecture, in particular at edge nodes. RDF processing at the edge facilitates the deployment of semantic integration gateways closer to low-level devices. Our focus is on how to enable scalable and robust RDF engines that can operate on lightweight devices. In this paper, we have first carried out an empirical study of the scalability and behaviour of solutions for RDF data management on standard computing hardware that have been ported to run on lightweight devices at the network edge. The findings of our study shows that these RDF store solutions have several shortcomings on commodity ARM (Advanced RISC Machine) boards that are representative of IoT edge node hardware. Consequently, this has inspired us to introduce a lightweight RDF engine, which comprises an RDF storage and a SPARQL processor for lightweight edge devices, called RDF4Led. RDF4Led follows the RISC-style (Reduce Instruction Set Computer) design philosophy. The design constitutes a flash-aware storage structure, an indexing scheme, an alternative buffer management technique and a low-memory-footprint join algorithm that demonstrates improved scalability and robustness over competing solutions. With a significantly smaller memory footprint, we show that RDF4Led can handle 2 to 5 times more data than popular RDF engines such as Jena TDB (Tuple Database) and RDF4J, while consuming the same amount of memory. In particular, RDF4Led requires 10%–30% memory of its competitors to operate on datasets of up to 50 million triples. On memory-constrained ARM boards, it can perform faster updates and can scale better than Jena TDB and Virtuoso. Furthermore, we demonstrate considerably faster query operations than Jena TDB and RDF4J.

Azimuthal illumination enabled ultra-high polar angle sensitivity by a Bloch surface wave resonance refractive index sensor

The scheme of a polar angle space refractive index (RI) sensor based on grating-coupled Bloch surface wave (BSW) resonance excited by azimuthal illuminations is studied. It is shown that, for any polar angle, one can always find a case of BSW resonance with the tangential wave vector of the BSW mode perpendicular to that of the incident electromagnetic wave. When the sensor is designed to work around this BSW resonance, ultra-high polar angle sensitivity can be expected. The numerical simulated polar angle sensitivity reaches as large as 1786 degrees per RI unit (Deg/RIU), which is almost one order higher than that found in the literature.