User-defined Queries Research Articles

A Learned Cuckoo Filter for Approximate Membership Queries over Variable-sized Sliding Windows on Data Streams

Designing a space-efficient data structure to answer membership queries while ensuring high accuracy and real-time response is a challenging task in the field of stream processing. Many techniques have been developed to answer these queries in a sliding windows manner. However, assuming the user will conduct the query with the presupposed window size is not always practical. In this paper, we introduce a novel data structure called Learned Cuckoo Filter (LCF). It can provide satisfactory results for the approximate membership query on data streams, regardless of the user-defined query windows. LCF operates by adaptively maintaining cuckoo filters with the assistance of a well-trained oracle that learned the frequency feature of the data within the stream. To further enhance memory utilization, we develop a compact version of LCF (denoted by LCF_C), which selectively removes redundant information to reduce space consumption without compromising query accuracy. Furthermore, we conduct a thorough theoretical analysis of query accuracy and provide detailed guidelines for optimal parameter selection (denoted by LCF_O). Extensive experimental studies on synthetic and real-world datasets demonstrate the superiority of the proposed methods in terms of both space consumption and accuracy. Compared to the state-of-the-art algorithms, LCF_O can reduce up to 61% of space cost at the same error level, and achieve up to 12× improved accuracy with the same space cost.

TrendFlow: A Machine Learning Framework for Research Trend Analysis

As various research fields continue to evolve, new technologies emerge constantly, making it challenging for scholars to keep up with the latest and most promising research directions. To address this issue, we propose TrendFlow, a framework that leverages machine learning and deep learning techniques for analyzing research trends. TrendFlow first searches relevant literature based on user-defined queries, then clusters the searched literature according to the abstracts, and finally generates keyphrases of the abstracts as research trends for each cluster. Our experimental results highlight the superior performance of TrendFlow compared to traditional literature analysis tools. We have released the beta version of TrendFlow on Huggingface.

Application of big data technology platform based on deep learning in smart tax evaluation system

In today’s modern transformation of tax affairs, as information technology and Internet technology are constantly updated and become more advanced, it is necessary to reasonably change the processing flow of tax affairs, such as breaking the time and space constraints to make it more convenient. It is an inevitable need to complete the reform and optimize the business environment through the “release, management and service” policy to achieve technological transformation. The big data technology platform can well accomplish the expected tasks, thus providing systematic support for tax work. In this context, this paper integrates the deep learning technology with the big data technology platform to complete the design of the mobile tax system for taxpayers. It is convenient and fast, and can realize the creation of an intelligent tax evaluation system, so as to carry out tax management, information retrieval, information release, user communication and system management, and can also safely repair the system itself. Through the design experiment, the user-defined query and retrieval function of the system is tested. The results show that the comprehensive query module of the system has good security, and the evaluation system can meet the user's requirements for ease of use and friendly system interface. This paper has completed the construction of smart tax evaluation system by integrating deep learning technology and big data technology platform.

STITCH enabled molecular interaction of glycrhizzin with peri-implant microbiota.

Glycyrrhizin is present in the root extract of the licorice plant used for various conditions, including gastric ulcers. Therefore, it is of interest to document the STITCH enabled molecular interaction of glycrhizzin with peri-implant microbiota. The peri implant pathogens includes Aggregatibacter actinomycetemcomitans (D7S-1), Centipeda periodontii, Campylobacter gracilisi, Fusobacterium nucleatumi, Slackia exigua, Prevotella intermedia, Tannerella forsythia, Staphylococcus aureus, Bacteroides fragilis, and Bacteroides fragilis. Hence, a user-defined query was used to conduct analysis on the provided bacterial strains whose molecular data available in the STITCH database. Thus, we used the STITCH tool to examine protein interactions and the VirulentPred tool to assess pathogenicity using the known molecular data on glycrhizzin and peri implant pathogens. Data shows that glycyrrhizin interacts with peri implant pathogens.

STITCH enabled protein-protein interactions between Phyllanthus emblica and peri-implant microbiome.

It is of interest to document the protein-protein interaction between Phyllanthus emblica and peri implant pathogens in the context of peri implant illness. The peri implant pathogens includes Aggregatibacter actinomycetemcomitans (D7S-1), Centipeda periodontii, Campylobacter gracilis, Fusobacterium nucleatum, Slackia exigua, Prevotella intermedia, Tannerella forsythia, Staphylococcus aureus, Bacteroides fragilis, and Bacteroides fragilis. Hence, a user-defined query was used to conduct analysis on the provided bacterial strains whose molecular data available in the STITCH database. Thus, we used the STITCH tool to examine protein interactions and the VirulentPred tool to assess pathogenicity using the known molecular data on Phyllanthus emblica and peri implant pathogens. Data shows that Phyllanthus emblica interacts with peri implant pathogens.

Towards Efficient and Privacy-Preserving User-Defined Skyline Query Over Single Cloud

Skyline queries, especially those variants that allow users to define their own query criteria, are very promising and practical techniques in multi-criteria decision making applications. Meanwhile, the growing data volume drives the service providers to outsource their data to the cloud for reaping economic benefits. However, privacy concerns compel the outsourced data to be encrypted and require to perform the skyline queries over encrypted data. To achieve the privacy-preserving skyline queries, many schemes were proposed in the literature. However, those existing solutions cannot fully support the user-defined query criteria in skyline queries, and most of them employ a two-server model to support skyline queries over ciphertexts, which needs multi-round communications between the deployed two servers. In this article, we propose a privacy-preserving user-defined skyline query scheme in a single-server model, which eliminates extra communications. Specifically, we first formally define the user-defined skyline query. Then, based on the idea of converting order relations into computing the inner products of two multi-dimensional points, we design three predicate encryption schemes. Finally, we adopt these predicate encryption schemes to construct our proposed scheme. Detailed security analysis shows that these predicate encryption schemes are selectively secure, and the proposed user-defined skyline query scheme is privacy-preserving. In addition, extensive experiments are conducted, and the results show that our proposed scheme outperforms the alternative scheme by up to an order of magnitude in terms of computational costs when performing user-defined skyline queries.

User Preference-Based Video Synopsis Using Person Appearance and Motion Descriptions.

During the last decade, surveillance cameras have spread quickly; their spread is predicted to increase rapidly in the following years. Therefore, browsing and analyzing these vast amounts of created surveillance videos effectively is vital in surveillance applications. Recently, a video synopsis approach was proposed to reduce the surveillance video duration by rearranging the objects to present them in a portion of time. However, performing a synopsis for all the persons in the video is not efficacious for crowded videos. Different clustering and user-defined query methods are introduced to generate the video synopsis according to general descriptions such as color, size, class, and motion. This work presents a user-defined query synopsis video based on motion descriptions and specific visual appearance features such as gender, age, carrying something, having a baby buggy, and upper and lower clothing color. The proposed method assists the camera monitor in retrieving people who meet certain appearance constraints and people who enter a predefined area or move in a specific direction to generate the video, including a suspected person with specific features. After retrieving the persons, a whale optimization algorithm is applied to arrange these persons reserving chronological order, reducing collisions, and assuring a short synopsis video. The evaluation of the proposed work for the retrieval process in terms of precision, recall, and F1 score ranges from 83% to 100%, while for the video synopsis process, the synopsis video length compared to the original video is decreased by 68% to 93.2%, and the interacting tube pairs are preserved in the synopsis video by 78.6% to 100%.

CORE

Complex Event Recognition (CER) systems are a prominent technology for finding user-defined query patterns over large data streams in real time. CER query evaluation is known to be computationally challenging, since it requires maintaining a set of partial matches, and this set quickly grows super-linearly in the number of processed events. We present CORE, a novel COmplex event Recognition Engine that focuses on the efficient evaluation of a large class of complex event queries, including time windows as well as the partition-by event correlation operator. This engine uses a novel automaton-based evaluation algorithm that circumvents the super-linear partial match problem: under data complexity, it takes constant time per input event to maintain a data structure that compactly represents the set of partial matches and, once a match is found, the query results may be enumerated from the data structure with output-linear delay. We experimentally compare CORE against state-of-the-art CER systems on real-world data. We show that (1) CORE's performance is stable with respect to both query and time window size, and (2) CORE outperforms the other systems by up to five orders of magnitude on different workloads.

Query-focused multi-document text summarization using fuzzy inference

The present paper proposes a fuzzy inference system for query-focused multi-document text summarization (MTS). The overall scheme is based on Mamdani Inferencing scheme which helps in designing Fuzzy Rule base for inferencing about the decision variable from a set of antecedent variables. The antecedent variables chosen for the task are from linguistic and positional heuristics, and similarity of the documents with the user-defined query. The decision variable is the rank of the sentences as decided by the rules. The final summary is generated by solving an Integer Linear Programming problem. For abstraction coreference resolution is applied on the input sentences in the pre-processing step. Although designed on the basis of a small set of antecedent variables the results are very promising.

DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues.

In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.

A hybrid query expansion framework for the optimal retrieval of the biomedical literature

With the proliferation of biomedical literature, it is quite challenging for biomedical scientists to keep them updated with the new advancements. In biomedical literature retrieval systems, the keywords in the user-defined queries are often defined with various lexical variants consequently leading to the vocabulary mismatch (VM). One possible way to cope with these issues is to introduce a query expansion (QE) framework to enrich the original queries with the auxiliary semantically similar terms for each keyword mentioned in a query. In this research, we propose a biomedical QE framework to alleviate the VM. The proposed approach combines the clinical diagnosis information (CDI) and word embeddings (WEs) simultaneously to retrieve the relevant biomedical literature. The process of embeddings vocabulary terms as real-valued and low dimensional vectors referred to as word embedding has garnered significant attention by potentially capturing the implicit semantics. We have exploited threefold word embeddings (Domain-Specific, Domain-Agnostic, and Hybrid) and integrated the embeddings outcomes with the CDI to get the best query combination for the efficient retrieval of biomedical literature. Experimental results procured for the Text REtrieval Conference dataset showed that CDI, when used with the hybrid word embeddings surpassed the WEs trained for the domain-specific and domain-agnostic data. The results demonstrate that the utilization of this unique setup of merging two techniques is a valuable addition to the QE process leading to significantly improved precision rate and VM in biomedical literature retrieval. We hope that our approach would assist investigators to use this query combination to retrieve relevant articles.

Novel Search Strategies for Conducting Systematic Literature Reviews: Comparing a Novel Citation-Based Combined Bfsearch Approach with Text-Based User-Defined Queries and Relevance Prediction Through Active Learning

Novel Search Strategies for Conducting Systematic Literature Reviews: Comparing a Novel Citation-Based Combined Bfsearch Approach with Text-Based User-Defined Queries and Relevance Prediction Through Active Learning

Searching Geometric Patterns in Protein Binding Sites and Their Application to Data Mining in Protein Kinase Structures.

The ever-growing number of protein-ligand complex structures can give fundamental insights into protein functions and protein-ligand interactions, especially in the field of protein kinase research. The number of tools to mine this data for individually defined structural motifs is restricted due to the challenging task of developing efficient index structures for 3D data in relational databases. Herein we present GeoMine, a database system with web front-end mining of more than 900 000 binding sites. It enables database searches for geometric (interaction) patterns in protein-ligand interfaces by, for example, textual, numerical, substructure, similarity, and 3D searches. GeoMine processes reasonably selective user-defined queries within minutes. We demonstrate its usability for advancing protein kinase research with a special emphasis on unusual interactions, their use in designing selective kinase inhibitors, and the analysis of reactive cysteine residues that are amenable to covalent kinase inhibitors. GeoMine is freely available as part of our modeling support server at https://proteins.plus.

Multi-level feature driven storage management of surveillance videos

Surveillance videos in smart environments have become commodities nowadays, which enable many novel applications, including various video analytics that turn videos into semantic results. In addition to live feeds, surveillance videos may be saved in a storage server for on-demand user-defined queries in the future. Different from on-demand video streaming servers, whose design objective is to maximize the user-perceived video quality, a surveillance video storage server has limited space and must retain as much information as possible while reserving sufficient space for incoming videos. In this article, we design, implement, optimize, and evaluate a multi-level feature driven storage server for diverse-scale smart environments, which can be buildings, campuses, communities, and cities. We focus on the design and implementation of the storage server and solve two key research problems in it, namely: (i) efficiently determining the information amount of incoming videos and (ii) intelligently deciding the qualities of videos to be kept. In particular, we first analyze the videos to derive approximate information amount without overloading our storage server. This is done by formally defining the information amount based on multi-level (semantic and visual) features of videos. We then leverage the information amounts to determine the optimal downsampling approach and target quality level of each video clip to save the storage space, while preserving as much information amount as possible. We rigorously formulate the above two research problems into mathematical optimization problems, and propose optimal, approximate, and efficient algorithms to solve them. Besides a suite of optimization algorithms, we also implement our proposed system on a smart campus testbed at NTHU, Taiwan, which consists of eight smart street lamps. The street lamps are equipped with a wide spectrum of sensors, network devices, analytics servers, and a storage server. We compare the performance of our proposed algorithms against the current practices using real surveillance videos from our smart campus testbed. Our efficient algorithms outperform the current practices in multiple dimensions, meaning we: (i) achieve a mere 7% approximation gap on captured information amount compared to the optimal solutions, (ii) save almost 3 times more clips after a week, (iii) achieve 58% less per-query error on average, (iv) always terminate in less than 100 ms, (v) do not consume excessive storage space, and (vi) scale well with larger storage spaces.

PU learning-based recognition of structural elements in architectural floor plans

This work introduces a computational method for the recognition of structural elements in architectural floor plans. The proposed method requires minimal user interaction and is capable of effectively analysing floor plans in order to identify different types of structural elements in various notation styles. It employs feature extraction based on Haar kernels and PU learning, in order to retrieve image regions, which are similar to a user-defined query. Most importantly, apart from this user-defined query, the proposed method is not dependent on learning from labelled samples. Therefore, there is no need for laborious annotations to form large datasets in various notation styles. The experimental evaluation has been performed on a publicly available and diverse dataset of floor plans. The results show that the proposed method outperforms a state-of-the-art method, with respect to retrieval accuracy. Further experiments on additional floor plans of various notation styles, demonstrate its general applicability.

Tree Index Nearest Neighbor Search of Moving Objects along a Road Network

With the widespread application of location‐based service (LBS) technology in the urban Internet of Things, urban transportation has become a research hotspot. One key issue of urban transportation is the nearest neighbor search of moving objects along a road network. The fast‐updating operations of moving objects along a road network suppress the query response time of urban services. Thus, a tree‐indexed searching method is proposed to quickly find the answers to user‐defined queries on frequently updating road networks. First, a novel index structure, called the double tree‐hash index, is designed to reorganize the corresponding relationships of moving objects and road networks. Second, an index‐enhanced search algorithm is proposed to quickly find the k‐nearest neighbors of moving objects along the road network. Finally, an experiment shows that compared with state‐of‐the‐art algorithms, our algorithm shows a significant improvement in search efficiency on frequently updating road networks.

Static generation of UML sequence diagrams

UML sequence diagrams are visual representations of object interactions in a system and can provide valuable information for program comprehension, debugging, maintenance, and software archeology. Sequence diagrams generated from legacy code are independent of existing documentation that may have eroded. We present a framework for static generation of UML sequence diagrams from object-oriented source code. The framework provides a query refinement system to guide the user to interesting interactions in the source code. Our technique involves constructing a hypergraph representation of the source code, traversing the hypergraph with respect to a user-defined query, and generating the corresponding set of sequence diagrams. We implemented our framework as a tool, StaticGen (supporting software: StaticGen), analyzing a corpus of 30 Android applications. We provide experimental results demonstrating the efficacy of our technique (originally appeared in the Proceedings of Fundamental Approaches to Software Engineering—20th International Conference, FASE 2017, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2017, Uppsala, Sweden, April 22–29, 2017).

Learning with fuzzy hypergraphs: A topical approach to query-oriented text summarization

Existing graph-based methods for extractive document summarization represent sentences of a corpus as the nodes of a graph in which edges depict relationships of lexical similarity between sentences. This approach fails to capture semantic similarities between sentences when they express a similar information but have few words in common and are thus lexically dissimilar. To overcome this issue, we propose to extract semantic similarities based on topical representations of sentences. Inspired by the Hierarchical Dirichlet Process, we propose a topic model to infer topic distributions of sentences. As each topic defines a semantic connection among sentences with a certain degree of membership for each sentence, we propose a fuzzy hypergraph model in which nodes are sentences and fuzzy hyperedges are topics. To produce an informative summary, we extract a set of sentences from the corpus by simultaneously maximizing their relevance to a user-defined query, their centrality in the fuzzy hypergraph and their coverage of topics present in the corpus. We formulate an algorithm building on the theory of submodular functions to solve the associated optimization problem. A thorough comparative analysis with other graph-based summarizers demonstrates the superiority of our method in terms of content coverage of the summaries.

RepEx: A web server to extract sequence repeats from protein and DNA sequences

Evolution builds up new genetic material from existing ones, not in random, but in highly ordered and eloquent patterns. Most of these sequence repeats are revelatory of valuable information contributing to areas of disease research and function of macromolecules, to name a few. In the age of next generation genome sequencing, rapid and efficient extraction of all unbiased sequence repeats from macromolecules is the need of the hour. In view of this reckoning, an online web-based computing server, RepEx, has been developed to extract and display all possible repeats for DNA and protein sequences. Apart from exact or identical repeats, the server has been designed adeptly to identify and extract degenerate, inverted, everted and mirror repeats from both DNA and protein sequences. The server has striking output displays, featuring interactive graphs and comprehensive output files. In addition, RepEx has been accoutered with an easy-to-use interface and search filters to facilitate a user-defined query or search and is freely available and accessible via the World Wide Web at http://bioserver2.physics.iisc.ac.in/RepEx/.

CoNVaQ: a web tool for copy number variation-based association studies

BackgroundCopy number variations (CNVs) are large segments of the genome that are duplicated or deleted. Structural variations in the genome have been linked to many complex diseases. Similar to how genome-wide association studies (GWAS) have helped discover single-nucleotide polymorphisms linked to disease phenotypes, the extension of GWAS to CNVs has aided the discovery of structural variants associated with human traits and diseases.ResultsWe present CoNVaQ, an easy-to-use web-based tool for CNV-based association studies. The web service allows users to upload two sets of CNV segments and search for genomic regions where the occurrence of CNVs is significantly associated with the phenotype. CoNVaQ provides two models: a simple statistical model using Fisher’s exact test and a novel query-based model matching regions to user-defined queries. For each region, the method computes a global q-value statistic by repeated permutation of samples among the populations. We demonstrate our platform by using it to analyze a data set of HPV-positive and HPV-negative penile cancer patients.ConclusionsCoNVaQ provides a simple workflow for performing CNV-based association studies. It is made available as a web platform in order to provide a user-friendly workflow for biologists and clinicians to carry out CNV data analysis without installing any software. Through the web interface, users are also able to analyze their results to find overrepresented GO terms and pathways. In addition, our method is also available as a package for the R programming language. CoNVaQ is available at https://convaq.compbio.sdu.dk.