Query Retrieval Research Articles

REBOR: A new sketch-based 3d object retrieval framework using retina inspired features

With the rapid development of data science and modeling engineering, the capacity of cyberspace has significantly expanded which enables the online storage of increasing number of 3D models. Hence, the development of effective and efficient approaches to search 3D models is becoming increasingly important and urgent. In this paper, we propose a new sketch-based 3D retrieval framework named REBOR under the inspiration of retina which is not only consistent with human perception sensitivity but also simplifies the requirement of retrieval query by enabling hand-drawn sketch. The feature extraction process incorporates human visual system by simulating the ganglion perceptive mechanism in retina. Support Vector Machine is used to classify the query sketches and is further optimized by means of an global optimization algorithm so as to acquire optimal results automatically. Experiments are done on the database generated by ourselves with 15 categories of 3D objects, and the results indicate the effectiveness of REBOR in terms of retrieval accuracy.

Indexed Dataset from YouTube for a Content-Based Video Search Engine

Numerous researches on content-based video indexing and retrieval besides video search engines are tied to a large-scaled video dataset. Unfortunately, reduction in open-sourced datasets resulted in complications for novel approaches exploration. Although, video datasets that index video files located on public video streaming services have other purposes, such as annotation, learning, classification, and other computer vision areas, with little interest in indexing public video links for purpose of searching and retrieval. This paper introduces a novel large-scaled dataset based on YouTube video links to evaluate the proposed content-based video search engine, gathered 1088 videos, that represent more than 65 hours of video, 11,000 video shots, and 66,000 unmarked and marked keyframes, 80 different object names used for marking. Moreover, a state-of-the-art features vector, and combinational-based matching, beneficial to the accuracy, speed, and precision of the video retrieval process. Any video record in the dataset is represented by three features: temporal combination vector, object combination vector with shot annotations, and 6 keyframes, sideways with other metadata. Video classification for the dataset was also imposed to expand the efficiency of retrieval of video-based queries. A two-phased approach has been used based on object and event classification, storing video records in aggregations related to feature vectors extracted. While object aggregation stores video records with the maximal occurrence of extracted object/concept from all shots, event aggregation classify based on groups according to the number of shots per video. This study indexed 58 out of 80 different object/concept categories, each has 9 shot number groups.

Density-Based Top-K Spatial Textual Clusters Retrieval

So-called spatial web queries retrieve web content representing points of interest, such that the points of interest have descriptions that are relevant to query keywords and are located close to a query location. Two broad categories of such queries exist. The first encompasses queries that retrieve single spatial web objects that each satisfy the query arguments. Most proposals belong to this category. The second category, to which this paper's proposal belongs, encompasses queries that support exploratory user behavior and retrieve sets of objects that represent regions of space that may be of interest to the user. Specifically, the paper proposes a new type of query, the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> spatial textual cluster retrieval ( <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -STC) query that returns the top- <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> clusters that (i) are located close to a query location, (ii) contain objects that are relevant with regard to given query keywords, and (iii) have an object density that exceeds a given threshold. To compute this query, we propose a DBSCAN-based approach and an OPTICS-based approach that rely on on-line density-based clustering and that exploit early stop conditions. Empirical studies on real data sets offer evidence that the paper's proposals can find good quality clusters and are capable of excellent performance.

Rapid single cell evaluation of human disease and disorder targets using REVEAL: SingleCell\u2122

BackgroundSingle-cell (sc) sequencing performs unbiased profiling of individual cells and enables evaluation of less prevalent cellular populations, often missed using bulk sequencing. However, the scale and the complexity of the sc datasets poses a great challenge in its utility and this problem is further exacerbated when working with larger datasets typically generated by consortium efforts. As the scale of single cell datasets continues to increase exponentially, there is an unmet technological need to develop database platforms that can evaluate key biological hypotheses by querying extensive single-cell datasets.Large single-cell datasets like Human Cell Atlas and COVID-19 cell atlas (collection of annotated sc datasets from various human organs) are excellent resources for profiling target genes involved in human diseases and disorders ranging from oncology, auto-immunity, as well as infectious diseases like COVID-19 caused by SARS-CoV-2 virus. SARS-CoV-2 infections have led to a worldwide pandemic with massive loss of lives, infections exceeding 7 million cases. The virus uses ACE2 and TMPRSS2 as key viral entry associated proteins expressed in human cells for infections. Evaluating the expression profile of key genes in large single-cell datasets can facilitate testing for diagnostics, therapeutics, and vaccine targets, as the world struggles to cope with the on-going spread of COVID-19 infections.Main bodyIn this manuscript we describe REVEAL: SingleCell, which enables storage, retrieval, and rapid query of single-cell datasets inclusive of millions of cells. The array native database described here enables selecting and analyzing cells across multiple studies. Cells can be selected using individual metadata tags, more complex hierarchical ontology filtering, and gene expression threshold ranges, including co-expression of multiple genes. The tags on selected cells can be further evaluated for testing biological hypotheses. One such example includes identifying the most prevalent cell type annotation tag on returned cells.We used REVEAL: SingleCell to evaluate the expression of key SARS-CoV-2 entry associated genes, and queried the current database (2.2 Million cells, 32 projects) to obtain the results in < 60 s. We highlighted cells expressing COVID-19 associated genes are expressed on multiple tissue types, thus in part explains the multi-organ involvement in infected patients observed worldwide during the on-going COVID-19 pandemic.ConclusionIn this paper, we introduce the REVEAL: SingleCell database that addresses immediate needs for SARS-CoV-2 research and has the potential to be used more broadly for many precision medicine applications. We used the REVEAL: SingleCell database as a reference to ask questions relevant to drug development and precision medicine regarding cell type and co-expression for genes that encode proteins necessary for SARS-CoV-2 to enter and reproduce in cells.

Towards Efficient and Intelligent Internet of Things Search Engine

The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.

Performance evaluation of reformulated query for information retrieval using real estate ontology

The data over the internet is growing at an unprecedented rate daily. Hence efficient information retrieval has always been at stake. The scenario becomes more tedious in an e-government sector like real estate where fetching of legal documentation used for buying and selling of land property is unknown to novice users. The uncommon legal terminology also dilutes the keyword-based retrieval system. The proposed system thus insists on creating and further utilising ontology in this semantic web era. The performance of information retrieval is measured by calculating various parameters like mean average precision, precision@k, and normalised discounted cumulative gain for general user query first and then comparing it with the reformulated query after applying the real estate ontology. The experimental results are further statistically checked to depict an improvement in all the parameters thereby indicating that an initial user query, after ontological reformulation improves the efficiency of the information retrieval process.

Performance evaluation of reformulated query for information retrieval using real estate ontology

The data over the internet is growing at an unprecedented rate daily. Hence efficient information retrieval has always been at stake. The scenario becomes more tedious in an e-government sector lik...

Deep Multiple Instance Hashing for Fast Multi-Object Image Search.

Multi-keyword query is widely supported in text search engines. However, an analogue in image retrieval systems, multi-object query, is rarely studied. Meanwhile, traditional object-based image retrieval methods often involve multiple steps separately. In this work, we propose a weakly-supervised Deep Multiple Instance Hashing (DMIH) approach for multi-object image retrieval. Our DMIH approach, which leverages a popular CNN model to build the end-to-end relation between a raw image and the binary hash codes of its multiple objects, can support multi-object queries effectively and integrate object detection with hashing learning seamlessly. We treat object detection as a binary multiple instance learning (MIL) problem and such instances are automatically extracted from multi-scale convolutional feature maps. We also design a conditional random field (CRF) module to capture both the semantic and spatial relations among different class labels. For hashing training, we sample image pairs to learn their semantic relationships in terms of hash codes of the most probable proposals for owned labels as guided by object predictors. The two objectives benefit each other in a multi-task learning scheme. Finally, a two-level inverted index method is proposed to further speed up the retrieval of multi-object queries. Our DMIH approach outperforms state-of-the-arts on public benchmarks for object-based image retrieval and achieves promising results for multi-object queries.

An information entropy and latent Dirichlet allocation approach to noise patent filtering

Defining valid patents in a particular technological field is an indispensable step in patent analysis. To minimise the risk of missing valid patents, domain experts manually exclude irrelevant patents, known as noise patents, from an initial patent set derived using a loose retrieval query. However, this task has become time-consuming and labour intensive due to the increasing number of patents and rising complexity of technological knowledge. This study proposes a semi-automated approach to noise patent filtering based on information entropy theory and latent Dirichlet allocation. The proposed approach comprises four discrete steps: (1) structuring patents using a term-weighting method; (2) recommending noise patent seeds based on the information quantity of patents in terms of focal keyword groups; (3) measuring text similarities for patent clustering using latent Dirichlet allocation; and (4) identifying potential noise patent clusters with respect to the noise patent seeds. Our case study confirms that the proposed approach is valuable as a complementary noise patent filtering tool that will enable domain experts to focus more on their own knowledge-intensive tasks such as prior art analysis and research and development (R&D) strategy formulation.

Deep learning-based detection and prediction of trending topics from streaming data

Detecting and predicting trending topics from steaming social data has always been the point of active research area in business and research firms to take quick decisions, change marketing strategies and set new goals. Topic modelling is one of the excellent methods to analyse the contents from large collection of documents in an unsupervised manner and it is a popular method used in natural language processing, information retrieval, text processing and many other research domains. In this paper, deep learning-based topic modelling technique has been proposed to detect and predict the trending topics from streaming data. The online version of latent semantic analysis with regularisation constraints has been designed using long short-term memory network. Specifically, a problem of detecting the topics from streaming media is handled as the minimisation of quadratic loss function constrained by l1 and l2 regularisation. The online learning mechanism supports scalable topic modelling. For topic prediction, sequence-to-sequence long short-term memory network has been designed. Experimentally, significant results have been achieved in terms of query retrieval performance and topic relevance metrics for topic detection on our published dataset. For topic prediction, the results obtained in terms of root mean squared error are also significant.

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.

Improving the representation of image descriptions for semantic image retrieval with RDF

The past few years have witnessed a surge of interest in many topics at the intersection of natural language processing and computer vision. In particular, using objects together with their attributes and relations to represent images or interpret languages has been proved useful across a wide variety of applications. The goal of this work is to provide an improved RDF-based model to represent images for enhancing textual based image retrieval. We use natural language processing tools to obtain a set of objects, attributes and relations; and then model them into graphical structures with RDF-based model. We also conduct some preliminary experiments to show how to handle textual based image retrieval for complex queries or multilingual queries. The experimental results show that our approach improves the representation of image descriptions, which is suitable for enhancing image retrieval with high-level semantics.

Secure Data Management in Cloudlet Assisted IoT Enabled e-Health Framework in Smart City

Internet of Things (IoT) is being adopted widely impacting every sphere around us. IoT enabled health-care is making the way ahead as it enables remote monitoring of health status seamlessly. The health-care industry historically has generated a large volume of data. With the induction of IoT, this volume has gone up drastically. Therefore, the health data is required to be stored properly to make sense of the data. In this paper, we propose cloudlet assisted IoT enabled e-Health framework. This e-Health framework aims to ease real-time data access using cloudlets. In this framework, we propose and implement a health-care data management scheme to store the enormous health data and process the queries for retrieval of the health data by end-users. We have used NoSQL based model to store health data. Performance of the proposed model has been studied in terms of data transmission time, energy consumption, query response time and data packet loss. Finally, by comparing the evaluation results of our proposed model with performances of the traditional cloud storage-based e-Health system, we justified that our proposed model is performing far better than existing cloud-based e-Health solutions.

Enhancing image retrieval for complex queries using external knowledge sources

Annotation-based image retrieval associates textual descriptions to images based on human perception. A user query, composed of keywords of choice and for retrieval, are usually matched lexically with the textual descriptions associated for stored images to extract the best matches. This paradigm will not produce appropriate desired results for complex queries if a semantic approach is not considered. This paper proposes an image retrieval framework which integrates external knowledge sources for obtaining a higher-level inference that can both handle complex queries and increase the number of relevant retrievals. The framework includes a parser where a semantic representation graph is initially generated from both image captions and query. The semantic representation of image captions is stored in the form of Resource Description Framework (RDF) triples, while the user query is translated into a SPARQL language query. For better query understanding, the external knowledge sources (ConceptNet, WordNet), are next fused together with the parser’s output in a significant process named query expansion to infer combined and expanded knowledge about the terms used in the query. Also, the expansion process generates a set of expansion rules to semantically expand the user query to adapt the inferred knowledge. The expanded query is matched against the stored RDF triplets to indicate the best matched image retrievals. Retrievals are eventually ranked using a relation similarity metric to obtain a ranked list of relevant images. Experimental studies carried on two Flickr datasets show that the proposed framework outperforms related work with 40% increase in the number of relevant retrievals at almost full accuracy. The framework achieves additionally an average increase for the accuracy at given k in the range of 50–72% for up to the tenth retrieval.

Dynamic Data Retrieval Using Incremental Clustering and Indexing

The evolution of the Internet and real-time applications has contributed to the growth of massive unstructured data which imposes the increased complexity of efficient retrieval of dynamic data. Extant research uses clustering methods and indexes to speed up the retrieval. However, the quality of clustering methods depends on data representation models where existing models suffer from dimensionality explosion and sparsity problems. As documents evolve, index reconstruction from scratch is expensive. In this work, compact vectors of documents generated by the Doc2Vec model are used to cluster the documents and the indexes are incrementally updated with less complexity using the diff method. The probabilistic ranking scheme BM25+ is used to improve the quality of retrieval for user queries. The experimental analysis demonstrates that the proposed system significantly improves the clustering performance and reduces retrieval time to obtain top-k results.

Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

A Cognitive Method for Automatically Retrieving Complex Information on a Large Scale

Modern retrieval systems tend to deteriorate because of their large output of useless and even misleading information, especially for complex search requests on a large scale. Complex information retrieval (IR) tasks requiring multi-hop reasoning need to fuse multiple scattered text across two or more documents. However, there are two challenges for multi-hop retrieval. To be specific, the first challenge is that since some important supporting facts have little lexical or semantic relationship with the retrieval query, the retriever often omits them; the second challenge is that once a retriever chooses misinformation related to the query as the entities of cognitive graphs, the retriever will fail. In this study, in order to improve the performance of retrievers in complex tasks, an intelligent sensor technique was proposed based on a sub-scope with cognitive reasoning (2SCR-IR), a novel method of retrieving reasoning paths over the cognitive graph to provide users with verified multi-hop reasoning chains. Inspired by the users’ process of step-by-step searching online, 2SCR-IR includes a dynamic fusion layer that starts from the entities mentioned in the given query, explores the cognitive graph dynamically built from the query and contexts, gradually finds relevant supporting entities mentioned in the given documents, and verifies the rationality of the retrieval facts. Our experimental results show that 2SCR-IR achieves competitive results on the HotpotQA full wiki and distractor settings, and outperforms the previous state-of-the-art methods by a more than two points absolute gain on the full wiki setting.

Query batching optimization in database systems

Techniques based on sharing data and computation among queries have been an active research topic in database systems. While work in this area developed algorithms and systems that are shown to be effective, there is a lack of rigorous modeling and theoretical study for query processing and optimization. Query batching in database systems has strong resemblance to order batching in the warehousing context. While the latter is a well-studied problem, the literature on optimization techniques for query batching problem is quite scarce/nonexistent. In this study, we develop a Mixed Binary Quadratic Program (MBQP) to optimize query-batching in a database system. This model uses the coefficients of a linear regression on the query retrieval time, trained by a large set of randomly generated query sets. We also propose two heuristics, the so-called restricted-cardinality search methods I and II, for solving the proposed MBQP. To demonstrate the effectiveness of our proposed techniques, we conduct a comprehensive computational study over randomly generated instances of three well-known database benchmarks. The computational results show that when the proposed MBQP is solved using the designed heuristics, an improvement of up to 61.8% in retrieving time is achieved.

A time efficient and accurate retrieval of range aggregate queries using fuzzy clustering means (FCM) approach

Massive growth in the big data makes difficult to analyse and retrieve the useful information from the set of available data’s. Statistical analysis: Existing approaches cannot guarantee an efficient retrieval of data from the database. In the existing work stratified sampling is used to partition the tables in terms of static variables. However k means clustering algorithm cannot guarantees an efficient retrieval where the choosing centroid in the large volume of data would be difficult. And less knowledge about the static variable might leads to the less efficient partitioning of tables. Findings: This problem is overcome in the proposed methodology by introducing the FCM clustering instead of k means clustering which can cluster the large volume of data which are similar in nature. Stratification problem is overcome by introducing the post stratification approach which will leads to efficient selection of static variable. Improvements: This methodology leads to an efficient retrieval process in terms of user query within less time and more accuracy.&lt;/p&gt;

Multiple Query Content-Based Image Retrieval Using Relevance Feature Weight Learning.

We propose a novel multiple query retrieval approach, named weight-learner, which relies on visual feature discrimination to estimate the distances between the query images and images in the database. For each query image, this discrimination consists of learning, in an unsupervised manner, the optimal relevance weight for each visual feature/descriptor. These feature relevance weights are designed to reduce the semantic gap between the extracted visual features and the user’s high-level semantics. We mathematically formulate the proposed solution through the minimization of some objective functions. This optimization aims to produce optimal feature relevance weights with respect to the user query. The proposed approach is assessed using an image collection from the Corel database.