Query Region Research Articles

Strain localization in hydraulic fracturing of granite specimens based on DIC method

Hydraulic fracturing is a crucial and widely-used technology in shale gas exploitation and enhanced geothermal systems, while the fundamental mechanisms of hydraulic fracturing are not comprehensively understood. This paper aims to promote the understanding of strain localization in hydraulic fracturing of granite specimens based on digital image correlation (DIC) method. Through a specially designed pressure enclosure, the hydraulic fracturing processes were captured by a high-speed camera. The strain localization phenomenon, evolution, and coalescence of SLZs in hydraulic fracturing were analyzed. Results show that obvious strain localization occurs before the specimen failure, which gradually develops into a continuous strain localization zone. With the evolution of SLZs, the specimen will be unable to bear the liquid pressure, and macro cracks occur at SLZs. The statistical data in the query region shows that the strain localization in hydraulic fracturing occurs earlier than previously thought, and the evolution of SLZs can be divided into steady, soaring, and fracture stages according to the changing trend. The coalescence of the patchy distribution SLZs occurs frequently throughout the evolution process of SLZs, and the coalescence of SLZs may be the cause of the increasing speed of evolution.

Financial Investment Recommendation in Coastal Areas Based on Improved Clustering Algorithm

Huang, Y.L., 2020. Financial investment recommendation in coastal areas based on improved clustering algorithm. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 215–218. Coconut Creek (Florida), ISSN 0749-0208.Based on the full analysis of the advantages and disadvantages of the traditional K - means and BIRCH clustering algorithms, an improved incremental clustering algorithm based on the core tree is proposed. The optimal global parameters Eps and MinPts are adaptively calculated according to the KNN distribution and mathematical statistics to avoid manual intervention in the clustering process so as to realize the full automation of the clustering process. By improving the seed selection method for regional query, no missing operation is needed to effectively improve the efficiency of clustering. The algorithm can helps financial users to make reasonable financial investment strategies in coastal areas, to a certain extent, reduce the financial investment risk, with strong practical significance.

Enhancer prediction in the human genome by probabilistic modelling of the chromatin feature patterns

BackgroundThe binding sites of transcription factors (TFs) and the localisation of histone modifications in the human genome can be quantified by the chromatin immunoprecipitation assay coupled with next-generation sequencing (ChIP-seq). The resulting chromatin feature data has been successfully adopted for genome-wide enhancer identification by several unsupervised and supervised machine learning methods. However, the current methods predict different numbers and different sets of enhancers for the same cell type and do not utilise the pattern of the ChIP-seq coverage profiles efficiently.ResultsIn this work, we propose a PRobabilistic Enhancer PRedictIoN Tool (PREPRINT) that assumes characteristic coverage patterns of chromatin features at enhancers and employs a statistical model to account for their variability. PREPRINT defines probabilistic distance measures to quantify the similarity of the genomic query regions and the characteristic coverage patterns. The probabilistic scores of the enhancer and non-enhancer samples are utilised to train a kernel-based classifier. The performance of the method is demonstrated on ENCODE data for two cell lines. The predicted enhancers are computationally validated based on the transcriptional regulatory protein binding sites and compared to the predictions obtained by state-of-the-art methods.ConclusionPREPRINT performs favorably to the state-of-the-art methods, especially when requiring the methods to predict a larger set of enhancers. PREPRINT generalises successfully to data from cell type not utilised for training, and often the PREPRINT performs better than the previous methods. The PREPRINT enhancers are less sensitive to the choice of prediction threshold. PREPRINT identifies biologically validated enhancers not predicted by the competing methods. The enhancers predicted by PREPRINT can aid the genome interpretation in functional genomics and clinical studies.

Identifying branch-specific positive selection throughout the regulatory genome using an appropriate proxy neutral

BackgroundAdaptive changes in cis-regulatory elements are an essential component of evolution by natural selection. Identifying adaptive and functional noncoding DNA elements throughout the genome is therefore crucial for understanding the relationship between phenotype and genotype.ResultsWe used ENCODE annotations to identify appropriate proxy neutral sequences and demonstrate that the conservativeness of the test can be modulated during the filtration of reference alignments. We applied the method to noncoding Human Accelerated Elements as well as open chromatin elements previously identified in 125 human tissues and cell lines to demonstrate its utility. Then, we evaluated the impact of query region length, proxy neutral sequence length, and branch count on test sensitivity and specificity. We found that the length of the query alignment can vary between 150 bp and 1 kb without affecting the estimation of selection, while for the reference alignment, we found that a length of 3 kb is adequate for proper testing. We also simulated sequence alignments under different classes of evolution and validated our ability to distinguish positive selection from relaxation of constraint and neutral evolution. Finally, we re-confirmed that a quarter of all non-coding Human Accelerated Elements are evolving by positive selection.ConclusionHere, we introduce a method we called adaptiPhy, which adds significant improvements to our earlier method that tests for branch-specific directional selection in noncoding sequences. The motivation for these improvements is to provide a more sensitive and better targeted characterization of directional selection and neutral evolution across the genome.

Grid-R-tree: a data structure for efficient neighborhood and nearest neighbor queries in data mining

The use of multi-dimensional indexing structures has gained a lot of attention in data mining. The most commonly used data structures for indexing data are R-tree and its variants, quad-tree, k-d-tree, etc. These data structures support region queries (point, window and neighborhood queries) and nearest neighbor queries. These queries are extensively used in data mining algorithms. Although these data structures facilitate execution of the above queries in logarithmic time, the constraints associated with them become bottleneck in query execution, when used for large and high-dimensional datasets. Moreover, these indexing structures do not cater to specific data access patterns of data mining algorithms. In this paper, we propose a new data structure Grid-R-tree, a grid based R-tree which is specifically designed to address the querying requirements of multiple data mining algorithms. Grid-R-tree is a simple, yet effective adaptation of R-tree using the concept of Grid. We also introduce a new query over Grid-R-tree, called cell-wise epsilon neighborhood query (CellWiseNBH), which captures the locality in query execution pattern of density-based clustering algorithms, and enables us to redesign them for improving their efficiency. Our theoretical and experimental analysis shows that the proposed data structure outperforms the conventional R-tree in terms of neighborhood and nearest neighbor queries. The experiments were conducted on datasets of size up to 100 million and dimensionality up to 74. The results also suggest that Grid-R-tree improves the efficiency of data mining algorithms such as k-nearest neighbor classifier and DBSCAN clustering (including the redesigned version that uses CellWiseNBH). Additionally, an adaptive grid optimization has been applied on dense cells that have number of indexed data points greater than a threshold $$\tau $$ to keep equal load distribution in the cells, which resulted in more efficient query performance for datasets that have skewed distribution of data points.

Energy analysis of Internet of things data mining algorithm for smart green communication networks

With the continuous development of Internet technology and electronic information technology, big data technology and cloud computing technology also rise and develop, and have a positive impact on people’s lives. Data mining system can deeply mine the value information contained in big data, so as to assist users to solve practical problems and help users to make correct decisions and judgments. This paper presents an energy analysis of data mining algorithm based on cloud platform for Internet of things (IoT). First of all, an improved Apriori algorithm is proposed, which is based on Boolean matrix and sorting index rules. Then Boolean matrix is obtained after scanning the data set and the Boolean matrix is preprocessed to delete the useless transactions and the item set, which are combined with sorting index to produce other item sets, effectively improving the efficiency of frequent item mining, which effectively reduce the memory usage. Secondly, the density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm needs human intervention in the global parameter selection, and the process of regional query is complex and the query is easy to lose objects. An improved parameter adaptive and regional query density clustering algorithm is proposed, which can effectively delete the redundant data in the high-level complex data space on the premise of retaining the internal nonlinear structure of the IoT data. The efficiency of clustering is also improved accordingly Finally, the simulation based on cloud platform verifies the effectiveness and superiority of the algorithm.

Sampling Big Trajectory Data for Traversal Trajectory Aggregate Query

This paper defines and investigates a novel trajectory query, namely, Traversal Trajectory Aggregate (TTA) Query: Given a trajectory database and a pair of upstream and downstream spatio-temporal (ST) regions (i.e., spatial area coupled with a time interval), a TTA query aims to retrieve the total number of unique trajectories that traverse through these two ST regions. Such TTA queries play an important role in various urban applications, such as route planning, taxi dispatching, and location-based advertising. Two baselines can answer such TTA queries: (a) exact search (over the entire ST query regions) can obtain the exact answer, but it leads to extremely long running time when the ST query regions are huge; (b) uniform-sampling-based approaches estimate the query answer with sampled trajectories. However, the uniform sampling distribution may lead to significant estimation variance for TTA query, because traversal trajectories are relatively few and unevenly distributed in the query regions. To tackle these challenges, this paper proposes a novel Targeted Index Sampling (TIS) framework to answer TTA queries with high estimation accuracy. TIS employs a two-stage framework, with a Pilot Sampling Estimation (PSE) stage to estimate the distribution of trajectories in ST query region, and an Integrated Importance Sampling (IIS) stage, which collects trajectories with the importance sampling distribution obtained in PSE, and estimates the query result with an asymptotically unbiased estimator. Extensive experiments and case studies using a large-scale real taxi trajectory dataset from Shenzhen, China demonstrate that our TIS framework achieves $\leq$ ≤ 10 percent estimation error with $\geq$ ≥ 90 percent computational time reduction over exact search, and 50 percent reduction on estimation error (with similar running time) over uniform-distribution-based sampling approaches.

Finding attribute-aware similar regions for data analysis

With the proliferation of mobile devices and location-based services, increasingly massive volumes of geo-tagged data are becoming available. This data typically also contains non-location information. We study how to use such information to characterize a region and then how to find a region of the same size and with the most similar characteristics. This functionality enables a user to identify regions that share characteristics with a user-supplied region that the user is familiar with and likes. More specifically, we formalize and study a new problem called the attribute-aware similar region search ( ASRS ) problem. We first define so-called composite aggregators that are able to express aspects of interest in terms of the information associated with a user-supplied region. When applied to a region, an aggregator captures the region's relevant characteristics. Next, given a query region and a composite aggregator, we propose a novel algorithm called DS-Search to find the most similar region of the same size. Unlike any previous work on region search, DS-Search repeatedly discretizes and splits regions until an split region either satisfies a drop condition or it is guaranteed to not contribute to the result. In addition, we extend DS-Search to solve the ASRS problem approximately. Finally, we report on extensive empirical studies that offer insight into the efficiency and effectiveness of the paper's proposals.

Comparative evaluation of region query strategies for DBSCAN clustering

Clustering is a technique that allows data to be organized into groups of similar objects. DBSCAN (Density-Based Spatial Clustering of Applications with Noise) constitutes a popular clustering algorithm that relies on a density-based notion of cluster and is designed to discover clusters of arbitrary shape. The computational complexity of DBSCAN is dominated by the calculation of the ϵ-neighborhood for every object in the dataset. Thus, the efficiency of DBSCAN can be improved in two different ways: (1) by reducing the overall number of ϵ-neighborhood queries (also known as region queries), or (2) by reducing the complexity of the nearest neighbor search conducted for each region query. This paper deals with the first issue by considering the most relevant region query strategies for DBSCAN, all of them characterized by inspecting the neighborhoods of only a subset of the objects in the dataset. We comparatively evaluate these region query strategies (or DBSCAN variants) in terms of clustering effectiveness and efficiency; additionally, a novel region query strategy is introduced in this work. The results show that some DBSCAN variants are only slightly inferior to DBSCAN in terms of effectiveness, while greatly improving its efficiency. Among these variants, the novel one outperforms the rest.

Content-based search of earth observation data archives using open-access multitemporal land cover and terrain products

Public Earth Observation (EO) data archives, e.g., MODIS, Landsat, and Sentinels, are valuable sources of information for a broad range of applications. For decision-supporting applications used in urban planning, land management, and sustainable development, images covering regions similar to the study area are prerequisites for high-accuracy decision making. These desirable images cannot be quickly searched for in the EO data archives via image metadata alone but can be obtained through content-based image retrieval methods. Land cover (LC) information, traditionally obtained through image segmentation or classification processing, is typically used in existing methods. Image processing is time consuming and has various accuracy levels for heterogeneous images, thus decreasing retrieval efficiency and accuracy. Additionally, the monotemporal LC information used has a limited ability to distinguish among confusable regions with different terrain, e.g., forests located on flatlands or mountains, and to obtain regions, e.g., urban regions, with similar growth rates. In this study, we employ free multiple-year 30 m LC products, a terrain product, and the Google Earth Engine (GEE) platform to accurately and efficiently locate the desired heterogeneous moderate spatial resolution images from various public EO data archives. Regions similar to the query region are detected with two-stage similarity calculations: First, monotemporal pixel-based LC and terrain information are used to filter out the most dissimilar regions; second, object-based LC change and terrain information are used to locate similar regions. Then, the desired images covering these detected similar regions are obtained from EO data archives via image metadata, e.g., geographical location and acquisition time. The experimental results of the two representative query regions show that our method can be used to obtain the desired images within several minutes and has higher accuracy than the LandEx method and a simplified method using only monotemporal LC information. The main contribution of our study is to reveal that LC changes and terrain information are helpful for improving the retrieval accuracy achieved from monotemporal LC information alone. Our method has great operability, with no need to perform EO data acquisition, image processing of raw EO images, or management of computational resources. Our method is conducive to making full use of images in various public EO archives to improve the decision making quality of decision-supporting applications.

Region-Based Active Learning with Hierarchical and Adaptive Region Construction.

Learning of classification models in practice often relies on human annotation effort in which humans assign class labels to data instances. As this process can be very time-consuming and costly, finding effective ways to reduce the annotation cost becomes critical for building such models. To solve this problem, instead of soliciting instance-based annotation we explore region-based annotation as the human feedback. A region is defined as a hyper-cubic subspace of the input space X and it covers a subpopulation of data instances that fall into this region. Each region is labeled with a number in [0,1] (in binary classification setting), representing a human estimate of the positive (or negative) class proportion in the subpopulation. To quickly discover pure regions (in terms of class proportion) in the data, we have developed a novel active learning framework that constructs regions in a hierarchical and adaptive way. Hierarchical means that regions are incrementally built into a hierarchical tree, which is done by repeatedly splitting the input space. Adaptive means that our framework can adaptively choose the best heuristic for each of the region splits. Through experiments on numerous datasets we demonstrate that our framework can identify pure regions in very few region queries. Thus our approach is shown to be effective in learning classification models from very limited human feedback.

Flood Risk Analysis on Terrains

An important problem in terrain analysis is modeling how water flows across a terrain and creates floods by filling up depressions. In this article, we study the flooding query problem: Preprocess a given terrain Σ, represented as a triangulated xy -monotone surface with n vertices, into a data structure so that for a query rain region R and a query point q on Σ, one can quickly determine how much rain has to fall in R so that q is flooded. Available terrain data is often subject to uncertainty, which must be incorporated into the terrain analysis. For instance, the digital elevation models of terrains have to be refined to incorporate underground pipes, tunnels, and waterways under bridges, but there is often uncertainty in their existence. By representing the uncertainty in the terrain data explicitly, we can develop methods for flood risk analysis that properly incorporate terrain uncertainty when reporting what areas are at risk of flooding. We present two results. First, we present an O ( n log n )-time algorithm for preprocessing Σ with a linear-size data structure that can answer a flooding query in O (| R | + m log n ) time, where | R | is the number of vertices in R , m is the number of so-called tributaries of q at which rain is falling, and n is the number of vertices of the terrain. Next, we extend this data structure to handle “uncertain” terrains using a standard Monte Carlo method. Given a probability distribution on terrain data, our data structure returns the probability of a query point being flooded if a specified amount of rain falls on a query region. We implement our data structure and test it on real terrains, showing that a small number of samples suffice to accurately estimate the flood risk.

Partial Enclosure Range Searching

A new type of range searching problem, called the partial enclosure range searching problem, is introduced in this paper. Given a set of geometric objects [Formula: see text] and a query region [Formula: see text], our goal is to identify those objects in [Formula: see text] which intersect the query region [Formula: see text] by at least a fixed proportion of their original size. Two variations of this problem are studied. In the first variation, the objects in [Formula: see text] are axis-parallel line segments and the goal is to count the total number of members of [Formula: see text] so that their intersection with [Formula: see text] is at least a given proportion of their size. Here, [Formula: see text] can be an axis-parallel rectangle or a parallelogram of arbitrary orientation. In the second variation, [Formula: see text] is a polygon and [Formula: see text] is an axis-parallel rectangle. The problem is to report the area of the intersection between the polygon [Formula: see text] and a query rectangle [Formula: see text].

LAZY R-tree: The R-tree with lazy splitting algorithm

The spatial index is a data structure formed according to the position and shape of the spatial object or the relationship between the spatial objects according to certain rules, and the spatial data is managed by an effective spatial data structure. The quality of a spatial index directly affects the performance of spatial queries. The R-tree index structure is a highly efficient spatial index. According to the R-tree query rule, when performing spatial query, most data that is not related to the query condition can be filtered out, and finally, a few leaf nodes can be accessed to query the data satisfying the condition. Its query performance is affected by factors such as non-leaf node overlap and node space utilisation. This article proposes a lazy splitting method to improve the R-tree construction process. The scheme works as follows: (1) When a node overflows, it creates an overflow node for that node and all overflow nodes are saved in a hash table. (2) If the node continues to insert data, the data are added to its overflow node. (3) When an overflow node is saturated, the node and its overflow node are split into two saturated nodes. We use both simulated and actual data to perform experiments. The experimental results show that an R-tree constructed by the lazy algorithm is superior to an R-tree constructed using the original R-tree PM algorithm or the corner-based splitting (CBS) algorithm based on the number of splits created, the node space used and the efficiency of region queries and k-nearest neighbour (kNN) queries.

Location-Based Seeds Selection for Influence Blocking Maximization in Social Networks

Influence blocking maximization (IBM) is a key problem for viral marketing in competitive social networks. Although the IBM problem has been extensively studied, existing works neglect the fact that the location information can play an important role in influence propagation. In this paper, we study the location-based seeds selection for IBM problem, which aims to find a positive seed set in a given query region to block the negative influence propagation in a given block region as much as possible. In order to overcome the low efficiency of the simulation-based greedy algorithm, we propose a heuristic algorithm IS-LSS and its improved version IS-LSS+, both of which are based on the maximum influence arborescence structure and Quadtree index, while IS-LSS+ further improves the efficiency of IS-LSS by using an upper bound method and Quadtree cell lists. The experimental results on real-world datasets demonstrate that our proposed algorithms are able to achieve matching blocking effect to the greedy algorithm as the increase in the number of positive seeds and often better than other heuristic algorithms, whereas they are four orders of magnitude faster than the greedy algorithm.

Continuous Region Query Processing in Clustered Point Sets

There exists an increasing usage rate of location-based information from mobile devices, which requires new query processing strategies. One such strategy is a continuous region query in which a moving user continuously sends queries to a central server to obtain data or information. In this paper, we introduce two strategies to process a spatial moving query over clustered data sets. Both strategies use a safe region approach on the client in order to minimize the number of queries that are sent to the server. We explore the use of a two-dimensional indexing strategy, as well as the use of Expectation Maximization (EM) and k-means clustering. Our experiments show that both strategies outperform a Baseline strategy where all queries are sent to the server, with respect to data transmission, running time and workload costs.

Research on why-not questions of top-K query in orthogonal region

Orthogonal region query has always been an important topic in the field of database query, geographic information system, computer graphics, data mining and multimedia information retrieval. In recent years, the “Why-Not” questions has gradually become a hot topic in the SQL query, Skyline query, and spatial keyword Top-K query. However, no one has answered the “Why-Not” questions of the orthogonal region Top-K query. Based on the in-depth study of the orthogonal region Top-K query algorithm, this paper first proposes to answer the “Why-Not” questions in the orthogonal region Top-K query. We adjust the initial query so that the result set of the new query contains the “Why-Not” elements with the least cost. Abundant experiments have been conducted to analyze the proposed algorithm on the factors of initial k value, initial rank, and data size. The experimental results demonstrate the accuracy and efficiency of the proposed algorithm.

Efficient region of visual interests search for geo-multimedia data

With the proliferation of online social networking services and mobile smart devices equipped with mobile communications module and position sensor module, massive amount of multimedia data has been collected, stored and shared. This trend has put forward higher request on massive multimedia data retrieval. In this paper, we investigate a novel spatial query named region of visual interests query (RoVIQ), which aims to search users containing geographical information and visual words. Three baseline methods are presented to introduce how to exploit existing techniques to address this problem. Then we propose the definition of this query and related notions at the first time. To improve the performance of query, we propose a novel spatial indexing structure called quadtree based inverted visual index which is a combination of quadtree, inverted index and visual words. Based on it, we design a efficient search algorithm named region of visual interests search to support RoVIQ. Experimental evaluations on real geo-image datasets demonstrate that our solution outperforms state-of-the-art method.

Reachable region query and its applications

In this paper we study the reachable region queries for objects moving in obstacle space. Loosely speaking, given any one of moving objects and many other information, we are asked to return the whole region in which each location has the non-zero probability to be reachable for the moving object. To solve this problem, this paper presents an efficient algorithm that runs in O(nlog n) time and consumes O(nlog n) space, where n is the number of vertices of obstacles. Moreover, this paper establishes the lower bound for the proposed problem, showing that our solution essentially is optimal in terms of random access memory (RAM) model. Empirical study based on both real and synthetic datasets demonstrates the effectiveness and efficiency of our solution.

Location-aware Influence Maximization over Dynamic Social Streams

Influence maximization (IM), which selects a set of k seed users (a.k.a., a seed set ) to maximize the influence spread over a social network, is a fundamental problem in a wide range of applications. However, most existing IM algorithms are static and location-unaware. They fail to provide high-quality seed sets efficiently when the social network evolves rapidly and IM queries are location-aware. In this article, we first define two IM queries, namely Stream Influence Maximization (SIM) and Location-aware SIM (LSIM), to track influential users over social streams. Technically, SIM adopts the sliding window model and maintains a seed set with the maximum influence value collectively over the most recent social actions. LSIM further considers social actions are associated with geo-tags and identifies a seed set that maximizes the influence value in a query region over a location-aware social stream. Then, we propose the Sparse Influential Checkpoints (SIC) framework for efficient SIM query processing. SIC maintains a sequence of influential checkpoints over the sliding window and each checkpoint maintains a partial solution for SIM in an append-only substream of social actions. Theoretically, SIC keeps a logarithmic number of checkpoints w.r.t. the size of the sliding window and always returns an approximate solution from one of the checkpoint for the SIM query at any time. Furthermore, we propose the Location-based SIC (LSIC) framework and its improved version LSIC + , both of which process LSIM queries by integrating the SIC framework with a Quadtree spatial index. LSIC can provide approximate solutions for both ad hoc and continuous LSIM queries in real time, while LSIC + further improves the solution quality of LSIC. Experimental results on real-world datasets demonstrate the effectiveness and efficiency of the proposed frameworks against the state-of-the-art IM algorithms.