Effective Index Structure Research Articles

Effective indexing for dynamic structural graph clustering

Graph clustering is a fundamental data mining task that clusters vertices into different groups. The structural graph clustering algorithm ( SCAN ) is a widely used graph clustering algorithm that derives not only clustering results, but also special roles of vertices like hubs and outliers. In this paper, we consider structural graph clustering on dynamic graphs under Jaccard similarity. The state-of-the-art index-based solution focuses on static graphs and incurs prohibitive update costs to maintain indices. Lately, an efficient approximate dynamic structural graph clustering algorithm Dyn-StrClu under Jaccard similarity is proposed. However, their solution needs to fix input parameters while parameter settings of SCAN usually need to be fine-tuned to achieve good clustering results. Motivated by these limitations, we present a study on devising effective index structures for SCAN algorithm on dynamic graphs. Similar to the state-of-the-art dynamic scheme, our main idea to reduce the time complexity is still by bringing approximation to clustering results. However, our solution does not need to fix the input parameters. To achieve this, our solution includes two key components. The first is to maintain a bottom- k sketch for each vertex so that the similarities of affected vertices can be easily updated. The second key is a bucketing strategy that allows us to update clustering results and roles of vertices efficiently. Our theoretical analysis shows that our proposed algorithm achieves O (log n · log M + m / pf ) expected update cost and guarantees to return approximate clustering results with probability 1 - pf after up to M updates. Extensive experiments show that our solution is up to two orders of magnitude faster than the state-of-the-art index-based solution while still achieving high-quality clustering results.

The most active community search in large temporal graphs

Community search enables users to find a dense subgraph containing a given query vertex from a large graph modeled network, which has recently received increasing attention. However, few works address the problem in a temporal graph although the vertices and the edges in many real networks often dynamically change over time. This paper introduces the problem known as the most active community search in large temporal graphs. Given a query vertex q and a cohesiveness constraint k, our objective is to find a set of vertices including q that can induce a maximally expanded community subject to some specified connectivity. Meanwhile, for each vertex in the community, the property that there are at least k other vertices co-occurring as its neighbors should be maintained on the most number of time points. To this end, we first propose a novel temporal active core model, i.e. (k,t)-active core, which requires every vertex has at least k neighbors co-occurring in no less than t snapshots. Given a fundamental solution to finding the active core containing q, we present a more efficient algorithm by proposing two upper-bound-based pruning rules. Based on this, we search the most active community containing q by three carefully designed search strategies, i.e., bottom-up search, top-down search, and binary search. Additionally, an effective index structure is devised to reduce the computational cost by limiting the search space to a much smaller range of the complete temporal graph. Finally, we demonstrate the efficiency and the effectiveness of our proposed algorithms and index by conducting extensive experiments on five real-world datasets.

Finding Route Hotspots in Large Labeled Networks

In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly connected owing to the similar characteristics, such as common habits and location proximity. In this article, we investigate the formation of hotspots from an alternative perspective that considers the routes along the network paths as the auxiliary information, and attempt to find the route hotspots in large labeled networks. A route hotspot is a cohesive subgraph that is covered by a set of routes, and these routes correspond to the same sequential pattern consisting of vertices' labels. To the best of our knowledge, the problem of Finding Route Hotspots in Large Labeled Networks has not been tackled in the literature. However, it is challenging as counting the number of hotspots in a network is #P-hard. Inspired by the observation that the sizes of hotspots decrease with the increasing lengths of patterns, we prove several anti-monotonicity properties of hotspots, and then develop a scalable algorithm called FastRH that can use these properties to effectively prune the patterns that cannot form any hotspots. In addition, to avoid the duplicate computation overhead, we judiciously design an effective index structure called RH-Index for storing the hotspot and pattern information collectively, which also enables incremental updating and efficient query processing. Our experimental results on real-world datasets clearly demonstrate the effectiveness and scalability of our proposed methods.

Super black iron nanostructures with broadband ultralow reflectance for efficient photothermal conversion

Structural absorption is widely accepted as an effective way to produce super black materials, which show matte black appearance and extremely broadband low reflectance. We have developed an ultrafast pulsed laser deposition (PLD) technology to deposit thin super black film by tuning the surface microstructure. Various coatings made by Fe, Al and Ti all showed broadband absorption, confirming that the PLD method is suitable for fabricating super back materials. At the wide range of wavelength from 250 nm to 2000 nm, the total integrated reflectance of iron coating is ~1.2%. The super black iron surface coating has low volume density composed of separated tree-like aggregates of nanoparticles. We have demonstrated that low effective refractive index and multi-layered iron oxide structures are the two contributing factors for the broadband ultralow reflectance. The broadband high-performance film was employed to effectively convert sunlight to heat. The steady-state temperature of the super black surface is 76 °C under solar irradiation.

Turbulent Structure Function Analysis Using Wireless Micro-Thermometer

Most of the application scenarios of techniques related to electromagnetic wave transmission of photoelectric system are located in the earth's atmosphere or through the atmospheric path. The electromagnetic wave will change state through the atmosphere when encounters the inhomogeneities of refractive index caused by turbulence. Optical turbulence causes degradation of beam quality and energy to laser transmission, and brings image deterioration to astronomical observation. The refractive index structure coefficient is an important parameter describing the turbulence strength. For visible and near infrared band, the refractive index structure coefficient mainly depends on temperature structure coefficient. Using the theory of Wheatstone bridge, the micro-thermometer is designed and self-developed. To avoid interference from human and buildings, the wireless control for the micro-thermometer is realized based on CC1100. The observation of turbulence strength of representative test point, for more than one month, is implemented at Yangmeikeng near the South China Sea. Compared to ultrasonic anemometer, there is a sensitive lower measurement limit of micro-thermometer whose effective refractive index structure coefficient of system noise is less than 10 -18 m -2/3 . There is obvious `Sombrero' structure diurnal variation of turbulence near South China Sea, whose strength is mainly brought out by buoyancy heat bubble in day, and by wind shear at night. Monin-Obukhov length is positive at night and negative in day, and the scaling exponent is near -5/3 for temperature power spectrum, which is similar to wind power spectrum except for periods when wind from inland. The diurnal variation and scaling exponent of power spectrum analysis indicate that the measurement range and the sample rate of micro-thermometer are enough to response the turbulence measurement encountered in most laser transmission and astronomical observation fields. The turbulence characteristics information gained from micro-thermometer measurement data analysis brings good reference to optimal design and operation for photoelectric system.

Efficient indexing and similarity search using the Geometric Near-neighbor Access Tree (GNAT) for Face-Images Data

Efficient access to large multimedia data sets is urgent need for face recognition systems. The feature vectors that characterize face in images data are often high-dimensional. Due to the high dimensions of feature vectors and the large quantity of images in the data set, efficient and effective indexing structures are necessary to speed up searching and querying operation. In this paper we present an efficient face recognition system by combination features indexed into a metric structure GNAT. Its principle, consists to describe each face of database by A threes features Local binary pattern, Fourier descriptor and Histogram of Oriented Gradient, afterward we index a normalized combined features into a GNAT. We use a similarity measure calculated as an affine linear combination of L1 and L2 metrics. Our experimental results show that our technique is both correct and efficient.

An efficient subscription index for publication matching in the cloud

Publish/subscribe has been successfully used in a variety of information dissemination applications. However, in a cloud computing environment, the enormous amount of information results in a very high requirement for the computing performance of a publish/subscribe method. In this paper, we propose an efficient index called Enindex for publish/subscribe matching. First, we group all the subscriptions submitted by subscribers, based on the key attributes (i.e., the most frequent attributes occurring in the subscriptions). Second, we group all the predicates contained in the subscriptions, according to three basic operators: ≥ (greater),=(equal), and ≤ (less), so as to remove the repeated predicates, and thus reduce the memory overhead. Finally, we propose an effective index structure to combine the grouped subscriptions together with the grouped predicates. Enindex not only has a small memory overhead, but also can support efficient publish/subscribe matching and online subscription updating. We conduct extensive experiments on synthetic datasets, and the experimental results demonstrate the superiority of the Enindex over state-of-the-art methods in terms of memory overhead and computing efficiency.

L-INDEX: A Conceptual Index of Textual Data on MapReduce Platform

Although in past few decades, lots of research has been done on searching documents in textual data and there have been many commercial systems for retrieving data on the Web. Fast retrieving desired documents from massive textual corpora is still challenging. Indexing is the most commonly used way to improve the performance of retrieving; particularly an effective index structure can help retrieving data more quickly and effectively. This work proposes a novel index structure, named as L−INDEX, to conceptually index massive set of textual data. L-INDEX employs Form Concept Analysis to discover the relationships between documents and expresses the discovered relationships with lattice. To make our approach more applicable to process massive set of data, the structure is implemented in MapReduce environment which can fulfill computing tasks in an efficient distributed way. A set of algorithms is developed for creating, maintaining, storing L-INDEX and searching documents through it as well. A serial of experiments is conducted to verify the performance of our approach by comparing it with some other popular existing indexing structures. The experimental results demonstrate that the proposed approach can index massive set of textual data with an effective structure which supports querying tasks in an efficient way.

Brain CT Image Similarity Retrieval Method Based on Uncertain Location Graph

A number of brain computed tomography (CT) images stored in hospitals that contain valuable information should be shared to support computer-aided diagnosis systems. Finding the similar brain CT images from the brain CT image database can effectively help doctors diagnose based on the earlier cases. However, the similarity retrieval for brain CT images requires much higher accuracy than the general images. In this paper, a new model of uncertain location graph (ULG) is presented for brain CT image modeling and similarity retrieval. According to the characteristics of brain CT image, we propose a novel method to model brain CT image to ULG based on brain CT image texture. Then, a scheme for ULG similarity retrieval is introduced. Furthermore, an effective index structure is applied to reduce the searching time. Experimental results reveal that our method functions well on brain CT images similarity retrieval with higher accuracy and efficiency.

Towards Mobile Document Image Retrieval for Digital Library

With the proliferation of mobile devices, recent years have witnessed an emerging potential to integrate mobile visual search techniques into digital library. Such a mobile application scenario in digital library has posed significant and unique challenges in document image search. The mobile photograph makes it tough to extract discriminative features from the landmark regions of documents, like line drawings, as well as text layouts. In addition, both search scalability and query delivery latency remain challenging issues in mobile document search. The former relies on an effective yet memory-light indexing structure to accomplish fast online search, while the latter puts a bit budget constraint of query images over the wireless link. In this paper, we propose a novel mobile document image retrieval framework, consisting of a robust Local Inner-distance Shape Context (LISC) descriptor of line drawings, a Hamming distance KD-Tree for scalable and memory-light document indexing, as well as a JBIG2 based query compression scheme, together with a Retinex based enhancement and an OTSU based binarization, to reduce the latency of delivering query while maintaining query quality in terms of search performance. We have extensively validated the key techniques in this framework by quantitative comparison to alternative approaches.

English

XML data format is widely used across devices to exchange data due to its main feature of supporting platform independency. Existing or current system used to find given keyword in XML data, a user need to know XML query language to create keyword query, submit the query for processing and get the results. In this approach user having limited knowledge about XML data, often feels complex and difficult to find require keyword in XML data and sometimes they has to use try and see approach to get relevant information. In this paper we will study different keyword based searching methods focusing on fuzzy search approach to find keyword in XML data. This searching techniques allows user explore data based on keyword as they typed even with minor mistakes in their keyword. Our survey paper helps to understand keyword based alternative method for searching XML data. Using this techniques user is able to find require sort of information from XML based file, without knowing XML query language and XML file content. Our survey paper focuses on techniques based on keyword search to retrieve top-k results, top-k algorithm to achieve high result quality and search efficiency and effective ranking method, early algorithm termination techniques while identifying top-k results and effective index structures.

Accelerating content-based image retrieval via GPU-adaptive index structure.

A tremendous amount of work has been conducted in content-based image retrieval (CBIR) on designing effective index structure to accelerate the retrieval process. Most of them improve the retrieval efficiency via complex index structures, and few take into account the parallel implementation of them on underlying hardware, making the existing index structures suffer from low-degree of parallelism. In this paper, a novel graphics processing unit (GPU) adaptive index structure, termed as plane semantic ball (PSB), is proposed to simultaneously reduce the work of retrieval process and exploit the parallel acceleration of underlying hardware. In PSB, semantics are embedded into the generation of representative pivots and multiple balls are selected to cover more informative reference features. With PSB, the online retrieval of CBIR is factorized into independent components that are implemented on GPU efficiently. Comparative experiments with GPU-based brute force approach demonstrate that the proposed approach can achieve high speedup with little information loss. Furthermore, PSB is compared with the state-of-the-art approach, random ball cover (RBC), on two standard image datasets, Corel 10 K and GIST 1 M. Experimental results show that our approach achieves higher speedup than RBC on the same accuracy level.

SR-Tree: An Index Structure of Sensor Management System for Spatial Approximate Query

Sensor management plays an important role in the field of Internet of Things. Therefore, the requests of spatial approximate query increase dramatically. Indexing is no doubt a feasible way for efficient spatial approximate search. However, there is a lack of an effective index structure for spatial approximate query. In this paper, we propose a new type of index structure called SR-tree for providing more intelligent retrieval, which is based on R-tree and inverted table. Our index can support for spatial approximate search and work freely either in memory or external memory. The experimental results show that the structure proposed can provide high scalability and fast response time.

A Kind of Index Technology Based on Binary Tree Efficient Space

Space index is one of the key technologies of spatial database, and also one of the biggest problems with puzzling GIS workers. So , how to build a more effective spatial index structure ,which has been the most realistic, most urgent, also to the forefront of research subject to GIS domain and graphics processing. This paper introduces several typical spatial index methods in the e GIS domain and graphics processing fields, and puts forward a binary tree index structure generated space grid step by step. The average time complexity of the index structure is the same with the quad tree index structure, but the maximum time complexity is reduced a third than the quadtree index structure, that is, the search efficiency improving a third.

An improved method of locality sensitive hashing for indexing large-scale and high-dimensional features

In recent years, Locality sensitive hashing (LSH) has been popularly used as an effective and efficient index structure of multimedia signals. LSH is originally proposed for resolving the high-dimensional approximate similarity search problem. Until now, many kinds of variations of LSH have been proposed for large-scale indexing. Much of the interest is focused on improving the query accuracy for skewed data distribution and reducing the storage space. However, when using LSH, a final filtering process based on exact similarity measure is needed. When the dataset is large-scale, the number of points to be filtered becomes large. As a result, the filtering speed becomes the bottleneck of improving the query speed when the scale of data becomes larger and larger. Furthermore, we observe a “Non-Uniform” phenomenon in the most popular Euclidean LSH which can degrade the filtering speed dramatically. In this paper, a pivot-based algorithm is proposed to improve the filtering speed by using triangle inequality to prune the search process. Furthermore, a novel method to select an optimal pivot for even larger improvement is provided. The experimental results on two open large-scale datasets show that our method can significantly improve the query speed of Euclidean LSH.

Efficient Fuzzy Type-Ahead Search in XML Data

In a traditional keyword-search system over XML data, a user composes a keyword query, submits it to the system, and retrieves relevant answers. In the case where the user has limited knowledge about the data, often the user feels “left in the dark” when issuing queries, and has to use a try-and-see approach for finding information. In this paper, we study fuzzy type-ahead search in XML data, a new information-access paradigm in which the system searches XML data on the fly as the user types in query keywords. It allows users to explore data as they type, even in the presence of minor errors of their keywords. Our proposed method has the following features: 1) Search as you type: It extends Autocomplete by supporting queries with multiple keywords in XML data. 2) Fuzzy: It can find high-quality answers that have keywords matching query keywords approximately. 3) Efficient: Our effective index structures and searching algorithms can achieve a very high interactive speed. We study research challenges in this new search framework. We propose effective index structures and top-k algorithms to achieve a high interactive speed. We examine effective ranking functions and early termination techniques to progressively identify the top-k relevant answers. We have implemented our method on real data sets, and the experimental results show that our method achieves high search efficiency and result quality.

An adaptive updating protocol for reducing moving object database workload

In the last decade, spatio-temporal database research focuses on the design of effective and efficient indexing structures in support of location-based queries such as predictive range queries and nearest neighbor queries. While a variety of indexing techniques have been proposed to accelerate the processing of updates and queries, not much attention has been paid to the updating protocol, which is another important factor affecting the system performance. In this paper, we propose a generic and adaptive updating protocol for moving object databases with less number of updates between objects and the database server, thereby reducing the overall workload of the system. In contrast to the approach adopted by most conventional moving object database systems where the exact locations and velocities last disclosed are used to predict their motions, we propose the concept of Spatio-temporal safe region to approximate possible future locations. Spatio-temporal safe regions provide larger space of tolerance for moving objects, freeing them from location and velocity updates as long as the errors remain predictable in the database. To answer predictive queries accurately, the server is allowed to probe the latest status of objects when their safe regions are inadequate in returning the exact query results. Spatio-temporal safe regions are calculated and optimized by the database server with two contradictory objectives: reducing update workload while guaranteeing query accuracy and efficiency. To achieve this, we propose a cost model that estimates the composition of active and passive updates based on historical motion records and query distribution. More system performance improvements can be obtained by cutting more updates from the clients, when the users of system are comfortable with incomplete but accuracy bounded query results. We have conducted extensive experiments to evaluate our proposal on a variety of popular indexing structures. The results confirm the viability, robustness, accuracy and efficiency of our proposed protocol.

An adaptive updating protocol for reducing moving object database workload

In the last decade, spatio-temporal database research focuses on the design of effective and efficient indexing structures in support of location-based queries such as predictive range queries and nearest neighbor queries. While a variety of indexing techniques have been proposed to accelerate the processing of updates and queries, not much attention has been paid to the updating protocol, which is another important factor affecting system performance. In this paper, we propose a generic and adaptive updating protocol for moving object databases with less number of updating messages between the objects and database server, thereby reducing the overall workload of the system. In contrast to the approach adopted by most conventional moving object database systems where the exact locations and velocities last disclosed are used to predict their motions, we propose the concept of Spatio-Temporal Safe Region to approximate possible future locations. Spatio-temporal safe regions provide larger space of tolerance for moving objects, freeing them from location and velocity updates as long as the errors remain predictable in the database. To answer predictive queries accurately, the server is allowed to probe the latest status of some moving objects when their safe regions are inadequate in returning the exact query results. Spatio-temporal safe regions are calculated and optimized by the database server with two contradictory objectives: reducing update workload while guaranteeing query accuracy and efficiency. To achieve this, we propose a cost model that estimates the composition of active and passive updates based on historical motion records and query distribution. We have conducted extensive experiments to evaluate our proposal on a variety of popular indexing structures. The results confirm the viability, robustness, accuracy and efficiency of our proposed protocol.

Historical index structure for reducing insertion and search cost in LBS

A major issue in LBS (Location Based Service) is the handling of numerous historical moving object data, affecting query performance and service quality in application systems. In order to store and search lots of data rapidly, an effective index structure is required for improving not only the insertion method, but also the search performance. In order to improve the performance of both applications, we propose the GIP + (Group Insertion tree with Projection Plus) for historical data management such as the trajectory of a vehicle. This index structure, based on the GIP, employs the separated buffer node method for reducing overlaps. The GIP + also uses projection storage for improving search performance by grouping the intersected child node in a node. Additionally, the link between the buffer nodes is designed to directly connect to the next buffer node. To effectively combine these methods and improve the performance, different node levels in the GIP + are also arranged for applying the separated buffer node, the projection storage, and the link. The designed historical index structure is useful for inserting and searching data which is arranged on a time axis.

MFI-tree: An effective multi-feature index structure for weighted query application

Multi-Feature Index Tree (MFI-Tree), a new indexing structure, is proposed to index multiple high-dimensional features of video data for video retrieval through example. MFI-Tree employs tree structure which is beneficial for the browsing application, and retrieves the last level cluster nodes in retrieval application to improve the performance. Aggressive Decided Distance for kNN (ADD-kNN) search algorithm is designed because it can effectively reduce the distance to prune the search space. Experimental results demonstrate that the MFITree and ADD-kNN algorithm have the advantages over sequential scan in performance.