Query Method Research Articles

A novel edge-centric approach for graph edit similarity computation

Graph similarity is an important notion with many applications. Graph edit distance is one of the most flexible graph similarity measures available. The main problem with this measure is that in practice it can only be computed for small graphs due to its exponential time complexity. This paper addresses the high complexity of graph edit distance computations. Specifically, we present CSI_GED, a novel edge-centric approach for computing graph edit distance through common sub-structure isomorphisms enumeration. CSI_GED utilizes backtracking search combined with a number of heuristics to reduce memory requirements and quickly prune away a large portion of the mapping search space. Experiments show that CSI_GED is highly efficient for computing graph edit distance; it outperforms the state-of-the-art methods by over three orders of magnitude. It also shows that CSI_GED scales the computation gracefully to larger and distant graphs on which current methods fail to run. Moreover, we evaluated CSI_GED as a stand-alone graph edit similarity search query method. The experiments show that CSI_GED is effective and scalable, and outperforms the state-of-the-art indexing-based methods by over two orders of magnitude.

Improving the Efficiency of Land Cover Classification by Combining Segmentation, Hierarchical Clustering, and Active Learning

Acquiring training samples for supervised machine learning methods to automate land cover classification is very labor intensive. The ever-increasing amount of available remote sensing data makes it necessary to reduce these costs. By adding a segmentation approach to hierarchical clustering and active learning, we create a new and unique method for land cover classification. In the segmentation stage, we adapt the simple linear iterative clustering algorithm to use the spectral angle instead of the Euclidean distance. The bisecting k -means algorithm creates the hierarchical clustering. Finally, we employ an active learning strategy based on the active queries method. We further investigate the effects of incorporating local density information into the active learning scheme. Here, we use probabilistic active learning. In this framework, the sample's label and its posterior probability are modeled as two random variables and weighted with the local density to estimate the impact of the sample's future label. We conduct eight experiments to test the influences of the method's different parameters and stages. Evaluation is done on three aerial remote sensing datasets for urban and vegetational regions in Germany: Abenberg, Potsdam, and Vaihingen. The method is compared to three state-of-the-art methods. Our results show that we are able to achieve the same overall accuracy while decreasing the amount of needed training samples by 95%.

Efficient Iris Recognition Management in Object-Related Databases

Biometric applications have grown significantly in recent years, particularly iris-based systems. In the present work, an extension of an Object Relational Database Management System for the integral management of a biometric system based on the human iris was presented. Although at present, there are many database extensions for different domains, in no case for biometric applications. The proposed extension includes both the extension of the type system and the definition of domain indexes for performance improvement. The aim of this work is to provide a tool that facilitates the development of biometric applications based on the iris feature. Its development is based on a reference architecture that includes both the management of images of the iris trait, its associated metadata and the necessary methods for both manipulation and queries. An implementation of the extension is performed for PostgreSQL DBMS, and SP-GiST framework is used in the implementation of a domain index. Experiments were carried out to evaluate the performance of the proposed index, which shows improvements in query execution times.

Video Searching and Fingerprint Detection by Using the Image Query and PlaceNet-Based Shot Boundary Detection Method

This work presents a novel shot boundary detection (SBD) method based on the Place-centric deep network (PlaceNet), with the aim of using video shots and image queries for video searching (VS) and fingerprint detection. The SBD method has three stages. In the first stage, we employed Local Binary Pattern-Singular Value Decomposition (LBP-SVD) features for candidate shot boundaries selection. In the second stage, we used the PlaceNet to select the shot boundary by semantic labels. In the third stage, we used the Scale-Invariant Feature Transform (SIFT) descriptor to eliminate falsely detected boundaries. The experimental results show that our SBD method is effective on a series of SBD datasets. In addition, video searching experiments are conducted by using one query image instead of video sequences. The results under several image transitions by using shot fingerprints have shown good precision.

Error-bounded and Number-bounded Approximate Spatial Query for Interactive Visualization

AbstractIn the big data era, an enormous amount of spatial and spatiotemporal data are generated every day. However, spatial query result sets that satisfy a query condition are very large, sometimes over hundreds or thousands of terabytes. Interactive visualization of big geospatial data calls for continuous query requests, and large query results prevent visual efficiency. Furthermore, traditional methods based on random sampling or line simplification are not suitable for spatial data visualization with bounded errors and bound vertex numbers. In this paper, we propose a vertex sampling method—the Balanced Douglas Peucker (B-DP) algorithm—to build hierarchical structures, where the order and weights of vertices are preserved in binary trees. Then, we develop query processing algorithms with bounded errors and bounded numbers, where the vertices are retrieved by binary trees’ breadth-first-searching (BFS) with a maximum-error-first (MEF) queue. Finally, we conduct an experimental study with OpenStreetMap (OSM) data to determine the effectiveness of our query method in interactive visualization. The results show that the proposed approach can markedly reduce the query results’ size and maintain high accuracy, and its performance is robust against the data volume.

A Method of HBase Multi-Conditional Query for Ubiquitous Sensing Applications.

Big data gathered from real systems, such as public infrastructure, healthcare, smart homes, industries, and so on, by sensor networks contain enormous value, and need to be mined deeply, which depends on a data storing and retrieving service. HBase is playing an increasingly important part in the big data environment since it provides a flexible pattern for storing extremely large amounts of unstructured data. Despite the fast-speed reading by RowKey, HBase does not natively support multi-conditional query, which is a common demand and operation in relational databases, especially for data analysis of ubiquitous sensing applications. In this paper, we introduce a method to construct a linear index by employing a Hilbert space-filling curve. As a RowKey generating schema, the proposed method maps multiple index-columns into a one-dimensional encoded sequence, and then constructs a new RowKey. We also provide a R-tree-based optimization to reduce the computational cost of encoding query conditions. Without using a secondary index mode, experimental results indicate that the proposed method has better performance in multi-conditional queries.

Supporting Continuous Skyline Queries in Dynamically Weighted Road Networks

The paper focuses on the design of an optimum method for handling the continuous skyline query problem in road networks. Existing studies on processing the continuous skyline query focus exclusively on static road networks, which are limited because the state of roads in road networks is constantly changing. Therefore, to apply current methods for dynamically weighted road networks, a distributed skyline query method based on a grid partition method has been proposed in this paper. The method adopts the concepts of a distributed computing framework and road network preprocessing computations in which multiple parallel computing nodes are allocated and organized in grids. Using this approach, the road network map is simplified to a hub graph with much smaller scale such that the query load of the central node can be significantly reduced. The theoretical analysis and experimental results both indicate that, by using the proposed method, the system can achieve quick response time for users as well as a good balance between response times and accuracy. Therefore, it can be concluded that using the proposed method is beneficial for handling continuous skyline queries in a dynamically weighted road network.

OPRCP: approximate nearest neighbor binary search algorithm for hybrid data over WMSN blockchain

In order to prevent sensitive data tampering in the application of security monitoring, intelligent traffic, and other sensitive Internet of Things, the research on WMSN (wireless multimedia sensor networks) application system based on blockchain and IPFS (InterPlanetary File System) is of great significance. However, WMSN data are characterized by high dimensionality, large scale, and multiple types, so it is challenging to search WMSN data efficiently over blockchain system. This paper proposed a novel One Permutation with Rotation and cross-polytope locality-sensitive hashing (OPRCP) method of approximate nearest neighbor binary query for querying binary hybrid data in the form of WMSN multimedia data (containing two hybrid types of data, such as image-text and image-audio). Firstly, a binary hybrid data index was built with the method of locality-sensitive hashing (LSH) to retain content similarity among original data objects for performing accurate queries. Secondly, the approximate nearest neighbor search strategy was used in place of the nearest neighbor strategy, to reduce querying time. Finally, a binary hybrid data model was employed to cope with multiple types of data in WMSN and carry out collaborative search of binary hybrid data. The experimental results show that compared with other mainstream methods, the proposed OPRCP method is widely adaptive to massive high-dimensional data in multiple types and can improve the accuracy of query results. The OPRCP method exhibits good performance, effectively saves resources, and reduces query time for a variety of datasets. It is an effective solution to the binary hybrid search of approximate neighbors, and it is applicable to the WMSN data search based on smart contracts in WMSN blockchain systems.

A Privacy Preserving Scheme for Nearest Neighbor Query.

In recent years, location privacy concerns that arise when using the nearest neighbor query services have gained increasing attention, as such services have become pervasive in mobile social networks devices and the IoT environments. State-of-the-art privacy preservation schemes focus on the obfuscation of the location information, which has suffered from various privacy attacks and the tradeoff of the quality of service. By noticing the fact that the user’s location could be replaced by their surrounding wireless sensor infrastructures in proximity, in this paper, we propose a wireless sensor access point-based scheme for the nearest neighbor query, without using the location of the user. Then, a noise-addition-based method that preserves user’s location privacy was proposed. To further strengthen the adaptability of the approach to real-world environments, several performance-enhancing methods are introduced, including an R-tree-based Noise-Data Retrieval Algorithm (RNR), and a nearest neighbor query method based on our research. Both performance and security evaluations are conducted to validate our approach. The results show the effectiveness and the practicality of our work.

Research on Improved Method of Storage and Query of Large-Scale Remote Sensing Images

The traditional method is used to deal with massive remote sensing data stored in low efficiency and poor scalability. This article presents a parallel processing method based on MapReduce and HBase. The filling of remote sensing images by the Hilbert curve makes the MapReduce method construct pyramids in parallel to reduce network communication between nodes. Then, the authors design a massive remote sensing data storage model composed of metadata storage model, index structure and filter column family. Finally, this article uses MapReduce frameworks to realize pyramid construction, storage and query of remote sensing data. The experimental results show that this method can effectively improve the speed of data writing and querying, and has good scalability.

Exploiting Social Network to Enhance Human-to-Human Infection Analysis without Privacy Leakage

Human-to-human infection, as a type of fatal public health threats, can rapidly spread, resulting in a large amount of labor and health cost for treatment, control and prevention. To slow down the spread of infection, social network is envisioned to provide detailed contact statistics to isolate susceptive people who has frequent contacts with infected patients. In this paper, we propose a novel human-to-human infection analysis approach by exploiting social network data and health data that are collected by social network and e-healthcare technologies. We enable the social cloud server and health cloud server to exchange social contact information of infected patients and user's health condition in a privacy-preserving way. Specifically, we propose a privacy-preserving data query method based on conditional oblivious transfer to guarantee that only the authorized entities can query users’ social data and the social cloud server cannot infer anything during the query. In addition, we propose a privacy-preserving classification-based infection analysis method that can be performed by untrusted cloud servers without accessing the users’ health data. The performance evaluation shows that the proposed approach achieves higher infection analysis accuracy with the acceptable computational overhead.

Efficient Learning-Free Keyword Spotting.

In this article, a method for segmentation-based learning-free Query by Example (QbE) keyword spotting on handwritten documents is proposed. The method consists of three steps, namely preprocessing, feature extraction and matching, which address critical variations of text images (e.g., skew, translation, different writing styles). During the feature extraction step, a sequence of descriptors is generated using a combination of a zoning scheme and a novel appearance descriptor, referred as modified Projections of Oriented Gradients. The preprocessing step, which includes contrast normalization and main-zone detection, aims to overcome the shortcomings of the appearance descriptor. Moreover, an uneven zoning scheme is introduced by applying a denser zoning only on query images for a more detailed representation. This leads to a significant reduction in storage requirements of a document collection. The distance between the query and word sequences is efficiently computed by the proposed Selective Matching algorithm. This algorithm is further extended to handle an augmented set of images originating from a single query image. The efficiency of the proposed method is demonstrated by experimentation conducted on seven publicly available datasets. In these experiments, the proposed method significantly outperforms all state-of-the-art learning-free techniques.

HPPQ: A parallel package queries processing approach for large-scale data

A lot of scholars have focused on developing effective techniques for package queries, and a lot of excellent approaches have been proposed. Unfortunately, most of the existing methods focus on a small volume of data. The rapid increase in data volume means that traditional methods of package queries find it difficult to meet the increasing requirements. To solve this problem, a novel optimization method of package queries (HPPQ) is proposed in this paper. First, the data is preprocessed into regions. Data preprocessing segments the dataset into multiple subsets and the centroid of the subsets is used for package queries, this effectively reduces the volume of candidate results. Furthermore, an efficient heuristic algorithm is proposed (namely IPOL-HS) based on the preprocessing results. This improves the quality of the candidate results in the iterative stage and improves the convergence rate of the heuristic algorithm. Finally, a strategy called HPR is proposed, which relies on a greedy algorithm and parallel processing to accelerate the rate of query. The experimental results show that our method can significantly reduce time consumption compared with existing methods.

Comparing and Contrasting Two ESSENCE Syndrome Definition Query Methods

Comparing and Contrasting Two ESSENCE Syndrome Definition Query Methods

Knowledge-based automated planning for oropharyngeal cancer.

The purpose of this study was to automatically generate radiation therapy plans for oropharynx patients by combining knowledge-based planning (KBP) predictions with an inverse optimization (IO) pipeline. We developed two KBP approaches, the bagging query (BQ) method and the generalized principal component analysis-based (gPCA) method, to predict achievable dose-volume histograms (DVHs). These approaches generalize existing methods by predicting physically feasible organ-at-risk (OAR) and target DVHs in sites with multiple targets. Using leave-one-out cross validation, we applied both models to a large dataset of 217 oropharynx patients. The predicted DVHs were input into an IO pipeline that generated treatment plans (BQ and gPCA plans) via an intermediate step that estimated objective function weights for an inverse planning model. The KBP predictions were compared to the clinical DVHs for benchmarking. To assess the complete pipeline, we compared the BQ and gPCA plans to both the predictions and clinical plans. To isolate the effect of the KBP predictions, we put clinical DVHs through the IO pipeline to produce clinical inverse optimized (CIO) plans. This approach also allowed us to estimate the complexity of the clinical plans. The BQ and gPCA plans were benchmarked against the CIO plans using DVH differences and clinical planning criteria. Iso-complexity plans (relative to CIO) were also generated and evaluated. The BQ method tended to predict that less dose is delivered than what was observed in the clinical plans while the gPCA predictions were more similar to clinical DVHs. Both populations of KBP predictions were reproduced with inverse plans to within a median DVH difference of 3Gy. Clinical planning criteria for OARs were satisfied most frequently by the BQ plans (74.4%), by 6.3% points more than the clinical plans. Meanwhile, target criteria were satisfied most frequently by the gPCA plans (90.2%), and by 21.2% points more than clinical plans. However, once the complexity of the plans was constrained to that of the CIO plans, the performance of the BQ plans degraded significantly. In contrast, the gPCA plans still satisfied more clinical criteria than both the clinical and CIO plans, with the most notable improvement being in target criteria. Our automated pipeline can successfully use DVH predictions to generate high-quality plans without human intervention. Between the two KBP methods, gPCA plans tend to achieve comparable performance as clinical plans, even when controlling for plan complexity, whereas BQ plans tended to underperform.

An advanced frequent closed sequences using BIDE

According to the past reasearches which produced few argumented stating that the frequent mining algorithm should only be closed but not frequent, as it not only results in compact but also complete results, and also in greater effectiveness. Most of the previous algorithms have mainly provided a direct test strategy to detect. In this article, we provide an Advanced BIDE, which is an effective algorithm used for processing query methods frequently closed. BI-Directional extension algorithm is better in pruning or filtering the search space when compared to any other algorithm. It is related to the calculation of frequent samples of search engines by parent-child relationships. An experimental study based on a variety of real historical data demonstrates the effectiveness and measurability of A-BIDE on the known alternatives of the past. It can also be scaled in terms of size of a query.

Incidence of hypersensitivity and anaphylaxis with sugammadex

Study objectiveTo evaluate the incidence of hypersensitivity and anaphylaxis after administration of sugammadex. DesignRetrospective analysis. SettingSugammadex clinical development program and post-marketing experience. PatientsSurgical patients and healthy volunteers who received sugammadex or placebo/comparator with anesthesia and/or neuromuscular blockade (NMB). InterventionsSugammadex administered as 2.0 mg/kg at reappearance of the second twitch, 4.0 mg/kg at 1–2 post-tetanic count, or 16.0 mg/kg at 3 min after rocuronium 1.2 mg/kg. MeasurementsThree analytical methods were used: 1) automated MedDRA queries; 2) searches of adverse events (AEs) consistent with treatment-related hypersensitivity reactions as diagnosed by the investigator; and 3) a retrospective adjudication of AEs suggestive of hypersensitivity by a blinded, independent adjudication committee (AC). In addition, a search of all post-marketing reports of events of hypersensitivity was performed, and events were retrospectively adjudicated by an independent AC. Anaphylaxis was determined according to Sampson Criterion 1. Main resultsThe pooled dataset included 3519 unique subjects who received sugammadex and 544 who received placebo. The automated MedDRA query method showed no apparent increase in hypersensitivity or anaphylaxis with sugammadex as compared to placebo or neostigmine. Similarly, there was a low overall incidence of AEs of treatment-related hypersensitivity (<1%), with no differences between sugammadex and placebo or neostigmine. Finally, the retrospective adjudication of AEs suggestive of hypersensitivity showed a low incidence of hypersensitivity (0.56% and 0.21% for sugammadex 2 mg/kg and 4 mg/kg, respectively), with an incidence similar to subjects who received placebo (0.55%). There were no confirmed cases of anaphylaxis in the pooled studies. During post-marketing use, spontaneous reports of anaphylaxis occurred with approximately 0.01% of sugammadex doses. ConclusionsSubjects who received sugammadex with general anesthesia and/or NMB had a low overall incidence of hypersensitivity, with no apparent increase in hypersensitivity or anaphylaxis with sugammadex as compared to placebo or neostigmine.

Three-dimensional stress wave imaging of wood internal defects using TKriging method

In order to detect the size, shape and degree of decay inside wood, a three-dimensional stress wave imaging method based on TKriging is proposed. The method uses sensors to obtain the stress wave velocity data sets by hanging around the timber randomly, and reconstructs the image of internal defect with those data sets. TKriging optimizes structural relationship between interpolation point and reference point in space firstly. The searching radius is used to select the reference points accordingly. Top-k query method is introduced to find the k value with relevant points. The values of the estimated points are calculated and three-dimensional image of the internal defect inside wood is reconstructed. The results show the effectiveness of the method and the accuracy rate is higher than that of basic Kriging method.

An extra spatial hierarchical schema in key-value store

The rapid growth of positioning technologies has resulted in an explosion of spatial data, and how to manage and retrieve such data has become a challenge. To solve this problem, many researchers pay attention to build spatial index in key-value store. Nevertheless, this will generate questions regarding the spatial index update and management. Furthermore, the efficiency of spatial query operations would decrease because it will generate much more request on network. Besides some scholars adopt space filling curve to carry out spatial query with primary key index, however it can bring out the questions of “Edge-Case Problem” and “Z-Order Problem” which is caused by space filling curve. To solve these questions, scholars resort to spatial index again. Nevertheless, we deem that the questions can be resolved without building spatial index. So this paper advocates an extra spatial hierarchical schema inspired by geohash, and design spatial query method based on primary keys index. Finally, to test the query accuracy and efficiency of the spatial hierarchical schema, we adopt Z-ordering, Hilbert, Row and Gray into the process of primary key encoding and conduct range query and k-NN queries in HBase. Experiment evaluation shows that the efficiency of the spatial queries good based on this schema even without the help of a spatial index.

A survey of real-time approximate nearest neighbor query over streaming data for fog computing

Real-time approximate nearest neighbor (ANN) query over streaming data in fog computing environment is the fundamental problem of real-time analysis of big data. As the fog computing paradigm needs to provide real-time and low latency services, and traditional streaming data ANN query technology cannot be directly applied. Exploring the basic theory, querying framework and technology of real-time ANN query over streaming data for fog computing becomes one of the current research hotspots. This paper summarizes the related ANN query technology based on random hash, learning-to-hash and synopses, analyzes the problems and challenges of real-time ANN query in resource-limited fog computing environment, and finally discusses in detail the basic theory and method of the query, the dimension reduction and encoding method based on learning-to-hash, the generating synopses method for ANN query over streaming data from Internet of Thing, and the future related research directions of ANN query framework and others. Additionally, we propose a Dynamic Adaptive Quantization (DAQ) method for learning-to-hash. Experiments show that DAQ outperformed other quantization methods.