当前期刊: The VLDB Journal Go to current issue    加入关注   
显示样式:        排序: 导出
  • SKCompress: compressing sparse and nonuniform gradient in distributed machine learning
    VLDB J. (IF 1.973) Pub Date : 2020-01-01
    Jiawei Jiang, Fangcheng Fu, Tong Yang, Yingxia Shao, Bin Cui

    Abstract Distributed machine learning (ML) has been extensively studied to meet the explosive growth of training data. A wide range of machine learning models are trained by a family of first-order optimization algorithms, i.e., stochastic gradient descent (SGD). The core operation of SGD is the calculation of gradients. When executing SGD in a distributed environment, the workers need to exchange local gradients through the network. In order to reduce the communication cost, a category of quantification-based compression algorithms are used to transform the gradients to binary format, at the expense of a low precision loss. Although the existing approaches work fine for dense gradients, we find that these methods are ill-suited for many cases where the gradients are sparse and nonuniformly distributed. In this paper, we study is there a compression framework that can efficiently handle sparse and nonuniform gradients? We propose a general compression framework, called SKCompress, to compress both gradient values and gradient keys in sparse gradients. Our first contribution is a sketch-based method that compresses the gradient values. Sketch is a class of algorithm that approximates the distribution of a data stream with a probabilistic data structure. We first use a quantile sketch to generate splits, sort gradient values into buckets, and encode them with the bucket indexes. Our second contribution is a new sketch algorithm, namely MinMaxSketch, which compresses the bucket indexes. MinMaxSketch builds a set of hash tables and solves hash collisions with a MinMax strategy. Since the bucket indexes are nonuniform, we further adopt Huffman coding to compress MinMaxSketch. To compress the keys of sparse gradients, the third contribution of this paper is a delta-binary encoding method that calculates the increment of the gradient keys and encode them with binary format. An adaptive prefix is proposed to assign different sizes to different gradient keys, so that we can save more space. We also theoretically discuss the correctness and the error bound of our proposed methods. To the best of our knowledge, this is the first effort utilizing data sketch to compress gradients in ML. We implement a prototype system in a real cluster of our industrial partner Tencent Inc. and show that our method is up to \(12\times \) faster than the existing methods.

  • $$\varvec{\textsc {Orpheus}}$$ORPHEUS DB: bolt-on versioning for relational databases (extended version)
    VLDB J. (IF 1.973) Pub Date : 2019-12-20
    Silu Huang, Liqi Xu, Jialin Liu, Aaron J. Elmore, Aditya Parameswaran

    Abstract Data science teams often collaboratively analyze datasets, generating dataset versions at each stage of iterative exploration and analysis. There is a pressing need for a system that can support dataset versioning, enabling such teams to efficiently store, track, and query across dataset versions. We introduce OrpheusDB, a dataset version control system that “bolts on” versioning capabilities to a traditional relational database system, thereby gaining the analytics capabilities of the database “for free.” We develop and evaluate multiple data models for representing versioned data, as well as a lightweight partitioning scheme, LyreSplit, to further optimize the models for reduced query latencies. With LyreSplit, OrpheusDB is on average \(10^3\times \) faster in finding effective (and better) partitionings than competing approaches, while also reducing the latency of version retrieval by up to \(20\times \) relative to schemes without partitioning. LyreSplit can be applied in an online fashion as new versions are added, alongside an intelligent migration scheme that reduces migration time by \(10\times \) on average.

  • Efficient query autocompletion with edit distance-based error tolerance
    VLDB J. (IF 1.973) Pub Date : 2019-12-14
    Jianbin Qin, Chuan Xiao, Sheng Hu, Jie Zhang, Wei Wang, Yoshiharu Ishikawa, Koji Tsuda, Kunihiko Sadakane

    Query autocompletion is an important feature saving users many keystrokes from typing the entire query. In this paper, we study the problem of query autocompletion that tolerates errors in users’ input using edit distance constraints. Previous approaches index data strings in a trie, and continuously maintain all the prefixes of data strings whose edit distances from the query string are within the given threshold. The major inherent drawback of these approaches is that the number of such prefixes is huge for the first few characters of the query string and is exponential in the alphabet size. This results in slow query response even if the entire query approximately matches only few prefixes. We propose a novel neighborhood generation-based method to process error-tolerant query autocompletion. Our proposed method only maintains a small set of active nodes, thus saving both space and time to process the query. We also study efficient duplicate removal, a core problem in fetching query answers, and extend our method to support top-k queries. Optimization techniques are proposed to reduce the index size. The efficiency of our method is demonstrated through extensive experiments on real datasets.

  • Efficient community discovery with user engagement and similarity
    VLDB J. (IF 1.973) Pub Date : 2019-10-26
    Fan Zhang, Xuemin Lin, Ying Zhang, Lu Qin, Wenjie Zhang

    Abstract In this paper, we investigate the problem of (k,r)-core which intends to find cohesive subgraphs on social networks considering both user engagement and similarity perspectives. In particular, we adopt the popular concept of k-core to guarantee the engagement of the users (vertices) in a group (subgraph) where each vertex in a (k,r)-core connects to at least k other vertices. Meanwhile, we consider the pairwise similarity among users based on their attributes. Efficient algorithms are proposed to enumerate all maximal (k,r)-cores and find the maximum (k,r)-core, where both problems are shown to be NP-hard. Effective pruning techniques substantially reduce the search space of two algorithms. A novel (\(k\),\(k'\))-core based (\(k\),\(r\))-core size upper bound enhances the performance of the maximum (k,r)-core computation. We also devise effective search orders for two algorithms with different search priorities for vertices. Besides, we study the diversified (\(k\),\(r\))-core search problem to find l maximal (\(k\),\(r\))-cores which cover the most vertices in total. These maximal (\(k\),\(r\))-cores are distinctive and informationally rich. An efficient algorithm is proposed with a guaranteed approximation ratio. We design a tight upper bound to prune unpromising partial (\(k\),\(r\))-cores. A new search order is designed to speed up the search. Initial candidates with large size are generated to further enhance the pruning power. Comprehensive experiments on real-life data demonstrate that the maximal (k,r)-cores enable us to find interesting cohesive subgraphs, and performance of three mining algorithms is effectively improved by all the proposed techniques.

  • Efficient distributed reachability querying of massive temporal graphs
    VLDB J. (IF 1.973) Pub Date : 2019-09-28
    Tianming Zhang, Yunjun Gao, Lu Chen, Wei Guo, Shiliang Pu, Baihua Zheng, Christian S. Jensen

    Reachability computation is a fundamental graph functionality with a wide range of applications. In spite of this, little work has as yet been done on efficient reachability queries over temporal graphs, which are used extensively to model time-varying networks, such as communication networks, social networks, and transportation schedule networks. Moreover, we are faced with increasingly large real-world temporal networks that may be distributed across multiple data centers. This state of affairs motivates the paper’s study of efficient reachability queries on distributed temporal graphs. We propose an efficient index, called Temporal Vertex Labeling (TVL), which is a labeling scheme for distributed temporal graphs. We also present algorithms that exploit TVL to achieve efficient support for distributed reachability querying over temporal graphs in Pregel-like systems. The algorithms exploit several optimizations that hinge upon non-trivial lemmas. Extensive experiments using massive real and synthetic temporal graphs are conducted to provide detailed insight into the efficiency and scalability of the proposed methods, covering both index construction and query processing. Compared with the state-of-the-art methods, the TVL based query algorithms are capable of up to an order of magnitude speedup with lower index construction overhead.

  • Skyline queries over incomplete data streams
    VLDB J. (IF 1.973) Pub Date : 2019-10-17
    Weilong Ren, Xiang Lian, Kambiz Ghazinour

    Abstract Nowadays, efficient and effective processing over massive stream data has attracted much attention from the database community, which are useful in many real applications such as sensor data monitoring, network intrusion detection, and so on. In practice, due to the malfunction of sensing devices or imperfect data collection techniques, real-world stream data may often contain missing or incomplete data attributes. In this paper, we will formalize and tackle a novel and important problem, named skyline query over incomplete data stream (Sky-iDS), which retrieves skyline objects (in the presence of missing attributes) with high confidences from incomplete data stream. In order to tackle the Sky-iDS problem, we will design efficient approaches to impute missing attributes of objects from incomplete data stream via differential dependency (DD) rules. We will propose effective pruning strategies to reduce the search space of the Sky-iDS problem, devise cost-model-based index structures to facilitate the data imputation and skyline computation at the same time, and integrate our proposed techniques into an efficient Sky-iDS query answering algorithm. Extensive experiments have been conducted to confirm the efficiency and effectiveness of our Sky-iDS processing approach over both real and synthetic data sets.

  • One-pass trajectory simplification using the synchronous Euclidean distance
    VLDB J. (IF 1.973) Pub Date : 2019-10-04
    Xuelian Lin, Jiahao Jiang, Shuai Ma, Yimeng Zuo, Chunming Hu

    Abstract Various mobile devices have been used to collect, store and transmit tremendous trajectory data, and it is known that raw trajectory data seriously wastes the storage, network bandwidth and computing resource. To attack this issue, one-pass line simplification (\(\textsf {LS} \)) algorithms have been developed, by compressing data points in a trajectory to a set of continuous line segments. However, these algorithms adopt the perpendicular Euclidean distance, and none of them uses the synchronous Euclidean distance (\(\textsf {SED} \)), and cannot support spatiotemporal queries. To do this, we develop two one-pass error bounded trajectory simplification algorithms (\(\textsf {CISED} \)-\(\textsf {S} \) and \(\textsf {CISED} \)-\(\textsf {W} \)) using \(\textsf {SED} \), based on a novel spatiotemporal cone intersection technique. Using four real-life trajectory datasets, we experimentally show that our approaches are both efficient and effective. In terms of running time, algorithms \(\textsf {CISED} \)-\(\textsf {S} \) and \(\textsf {CISED} \)-\(\textsf {W} \) are on average 3 times faster than \(\textsf {SQUISH} \)-\(\textsf {E} \) (the fastest existing \(\textsf {LS} \) algorithm using \(\textsf {SED} \)). In terms of compression ratios, \(\textsf {CISED} \)-\(\textsf {S} \) is close to and \(\textsf {CISED} \)-\(\textsf {W} \) is on average \(19.6\%\) better than \(\textsf {DPSED} \) (the existing sub-optimal \(\textsf {LS} \) algorithm using \(\textsf {SED} \) and having the best compression ratios), and they are \(21.1\%\) and \(42.4\%\) better than \(\textsf {SQUISH} \)-\(\textsf {E} \) on average, respectively.

  • Coconut: sortable summarizations for scalable indexes over static and streaming data series
    VLDB J. (IF 1.973) Pub Date : 2019-09-25
    Haridimos Kondylakis, Niv Dayan, Kostas Zoumpatianos, Themis Palpanas

    Many modern applications produce massive streams of data series that need to be analyzed, requiring efficient similarity search operations. However, the state-of-the-art data series indexes that are used for this purpose do not scale well for massive datasets in terms of performance, or storage costs. We pinpoint the problem to the fact that existing summarizations of data series used for indexing cannot be sorted while keeping similar data series close to each other in the sorted order. To address this problem, we present Coconut, the first data series index based on sortable summarizations and the first efficient solution for indexing and querying streaming series. The first innovation in Coconut is an inverted, sortable data series summarization that organizes data series based on a z-order curve, keeping similar series close to each other in the sorted order. As a result, Coconut is able to use bulk loading and updating techniques that rely on sorting to quickly build and maintain a contiguous index using large sequential disk I/Os. We then explore prefix-based and median-based splitting policies for bottom-up bulk loading, showing that median-based splitting outperforms the state of the art, ensuring that all nodes are densely populated. Finally, we explore the impact of sortable summarizations on variable-sized window queries, showing that they can be supported in the presence of updates through efficient merging of temporal partitions. Overall, we show analytically and empirically that Coconut dominates the state-of-the-art data series indexes in terms of construction speed, query speed, and storage costs.

  • Parsing gigabytes of JSON per second
    VLDB J. (IF 1.973) Pub Date : 2019-10-11
    Geoff Langdale, Daniel Lemire

    Abstract JavaScript Object Notation or JSON is a ubiquitous data exchange format on the web. Ingesting JSON documents can become a performance bottleneck due to the sheer volume of data. We are thus motivated to make JSON parsing as fast as possible. Despite the maturity of the problem of JSON parsing, we show that substantial speedups are possible. We present the first standard-compliant JSON parser to process gigabytes of data per second on a single core, using commodity processors. We can use a quarter or fewer instructions than a state-of-the-art reference parser like RapidJSON. Unlike other validating parsers, our software (simdjson) makes extensive use of single instruction and multiple data instructions. To ensure reproducibility, simdjson is freely available as open-source software under a liberal license.

  • Parallelizing approximate single-source personalized PageRank queries on shared memory
    VLDB J. (IF 1.973) Pub Date : 2019-10-08
    Runhui Wang, Sibo Wang, Xiaofang Zhou

    Abstract Given a directed graph G, a source node s, and a target node t, the personalized PageRank (PPR) \(\pi (s,t)\) measures the importance of node t with respect to node s. In this work, we study the single-source PPR query, which takes a source node s as input and outputs the PPR values of all nodes in G with respect to s. The single-source PPR query finds many important applications, e.g., community detection and recommendation. Deriving the exact answers for single-source PPR queries is prohibitive, so most existing work focuses on approximate solutions. Nevertheless, existing approximate solutions are still inefficient, and it is challenging to compute single-source PPR queries efficiently for online applications. This motivates us to devise efficient parallel algorithms running on shared-memory multi-core systems. In this work, we present how to efficiently parallelize the state-of-the-art index-based solution FORA, and theoretically analyze the complexity of the parallel algorithms. Theoretically, we prove that our proposed algorithm achieves a time complexity of \(O(W/P+\log ^2{n})\), where W is the time complexity of sequential FORA algorithm, P is the number of processors used, and n is the number of nodes in the graph. FORA includes a forward push phase and a random walk phase, and we present optimization techniques to both phases, including effective maintenance of active nodes, improving the efficiency of memory access, and cache-aware scheduling. Extensive experimental evaluation demonstrates that our solution achieves up to 37\(\times \) speedup on 40 cores and 3.3\(\times \) faster than alternatives on 40 cores. Moreover, the forward push alone can be used for local graph clustering, and our parallel algorithm for forward push is 4.8\(\times \) faster than existing parallel alternatives.

  • General dynamic Yannakakis: conjunctive queries with theta joins under updates
    VLDB J. (IF 1.973) Pub Date : 2019-11-19
    Muhammad Idris, Martín Ugarte, Stijn Vansummeren, Hannes Voigt, Wolfgang Lehner

    Abstract The ability to efficiently analyze changing data is a key requirement of many real-time analytics applications. In prior work, we have proposed general dynamic Yannakakis (GDyn), a general framework for dynamically processing acyclic conjunctive queries with \(\theta \)-joins in the presence of data updates. Whereas traditional approaches face a trade-off between materialization of subresults (to avoid inefficient recomputation) and recomputation of subresults (to avoid the potentially large space overhead of materialization), GDyn is able to avoid this trade-off. It intelligently maintains a succinct data structure that supports efficient maintenance under updates and from which the full query result can quickly be enumerated. In this paper, we consolidate and extend the development of GDyn. First, we give full formal proof of GDyn ’s correctness and complexity. Second, we present a novel algorithm for computing GDyn query plans. Finally, we instantiate GDyn to the case where all \(\theta \)-joins are inequalities and present extended experimental comparison against state-of-the-art engines. Our approach performs consistently better than the competitor systems with multiple orders of magnitude improvements in both time and memory consumption.

  • Top- k relevant semantic place retrieval on spatiotemporal RDF data
    VLDB J. (IF 1.973) Pub Date : 2019-11-19
    Dingming Wu, Hao Zhou, Jieming Shi, Nikos Mamoulis

    Abstract RDF data are traditionally accessed using structured query languages, such as SPARQL. However, this requires users to understand the language as well as the RDF schema. Keyword search on RDF data aims at relieving users from these requirements; users only input a set of keywords, and the goal is to find small RDF subgraphs that contain all keywords. At the same time, popular RDF knowledge bases also include spatial and temporal semantics, which opens the road to spatiotemporal-based search operations. In this work, we propose and study novel keyword-based search queries with spatial semantics on RDF data, namely kSP queries. The objective of the kSP query is to find RDF subgraphs which contain the query keywords and are rooted at spatial entities close to the query location. To add temporal semantics to the kSP query, we propose the kSPT query that uses two ways to incorporate temporal information. One way is considering the temporal differences between the keyword-matched vertices and the query timestamp. The other way is using a temporal range to filter keyword-matched vertices. The novelty of kSP and kSPT queries is that they are spatiotemporal-aware and that they do not rely on the use of structured query languages. We design an efficient approach containing two pruning techniques and a data preprocessing technique for the processing of kSP queries. The proposed approach is extended and improved with four optimizations to evaluate kSPT queries. Extensive empirical studies on two real datasets demonstrate the superior and robust performance of our proposals compared to baseline methods.

  • Making data visualization more efficient and effective: a survey
    VLDB J. (IF 1.973) Pub Date : 2019-11-19
    Xuedi Qin, Yuyu Luo, Nan Tang, Guoliang Li

    Data visualization is crucial in today’s data-driven business world, which has been widely used for helping decision making that is closely related to major revenues of many industrial companies. However, due to the high demand of data processing w.r.t. the volume, velocity, and veracity of data, there is an emerging need for database experts to help for efficient and effective data visualization. In response to this demand, this article surveys techniques that make data visualization more efficient and effective. (1) Visualization specifications define how the users can specify their requirements for generating visualizations. (2) Efficient approaches for data visualization process the data and a given visualization specification, which then produce visualizations with the primary target to be efficient and scalable at an interactive speed. (3) Data visualization recommendation is to auto-complete an incomplete specification, or to discover more interesting visualizations based on a reference visualization.

  • Adaptive partitioning and indexing for in situ query processing
    VLDB J. (IF 1.973) Pub Date : 2019-11-15
    Matthaios Olma, Manos Karpathiotakis, Ioannis Alagiannis, Manos Athanassoulis, Anastasia Ailamaki

    Abstract The constant flux of data and queries alike has been pushing the boundaries of data analysis systems. The increasing size of raw data files has made data loading an expensive operation that delays the data-to-insight time. To alleviate the loading cost, in situ query processing systems operate directly over raw data and offer instant access to data. At the same time, analytical workloads have increasing number of queries. Typically, each query focuses on a constantly shifting—yet small—range. As a result, minimizing the workload latency requires the benefits of indexing in in situ query processing. In this paper, we present an online partitioning and indexing scheme, along with a partitioning and indexing tuner tailored for in situ querying engines. The proposed system design improves query execution time by taking into account user query patterns, to (i) partition raw data files logically and (ii) build lightweight partition-specific indexes for each partition. We build an in situ query engine called Slalom to showcase the impact of our design. Slalom employs adaptive partitioning and builds non-obtrusive indexes in different partitions on-the-fly based on lightweight query access pattern monitoring. As a result of its lightweight nature, Slalom achieves efficient query processing over raw data with minimal memory consumption. Our experimentation with both microbenchmarks and real-life workloads shows that Slalom outperforms state-of-the-art in situ engines and achieves comparable query response times with fully indexed DBMS, offering lower cumulative query execution times for query workloads with increasing size and unpredictable access patterns.

  • Evaluating interactive data systems
    VLDB J. (IF 1.973) Pub Date : 2019-11-13
    Protiva Rahman, Lilong Jiang, Arnab Nandi

    Abstract Interactive query interfaces have become a popular tool for ad hoc data analysis and exploration. Compared with traditional systems that are optimized for throughput or batched performance, these systems focus more on user-centric interactivity. This poses a new class of performance challenges to the backend, which are further exacerbated by the advent of new interaction modes (e.g., touch, gesture) and query interface paradigms (e.g., sliders, maps). There is, thus, a need to clearly articulate the evaluation space for interactive systems. In this paper, we extensively survey the literature to guide the development and evaluation of interactive data systems. We highlight unique characteristics of interactive workloads, discuss confounding factors when conducting user studies, and catalog popular metrics for evaluation. We further delineate certain behaviors not captured by these metrics and propose complementary ones to provide a complete picture of interactivity. We demonstrate how to analyze and employ user behavior for system enhancements through three case studies. Our survey and case studies motivate the need for behavior-driven evaluation and optimizations when building interactive interfaces.

  • Correction: A survey of community search over big graphs
    VLDB J. (IF 1.973) Pub Date : 2019-11-11
    Yixiang Fang, Xin Huang, Lu Qin, Ying Zhang, Wenjie Zhang, Reynold Cheng, Xuemin Lin

    In the original article, the Table 1 was published with incorrect figures. The correct Table 1 is given below

  • Cleaning data with Llunatic
    VLDB J. (IF 1.973) Pub Date : 2019-11-08
    Floris Geerts, Giansalvatore Mecca, Paolo Papotti, Donatello Santoro

    Abstract Data cleaning (or data repairing) is considered a crucial problem in many database-related tasks. It consists in making a database consistent with respect to a given set of constraints. In recent years, repairing methods have been proposed for several classes of constraints. These methods, however, tend to hard-code the strategy to repair conflicting values and are specialized toward specific classes of constraints. In this paper, we develop a general chase-based repairing framework, referred to as Llunatic, in which repairs can be obtained for a large class of constraints and by using different strategies to select preferred values. The framework is based on an elegant formalization in terms of labeled instances and partially ordered preference labels. In this context, we revisit concepts such as upgrades, repairs and the chase. In Llunatic, various repairing strategies can be slotted in, without the need for changing the underlying implementation. Furthermore, Llunatic is the first data repairing system which is DBMS-based. We report experimental results that confirm its good scalability and show that various instantiations of the framework result in repairs of good quality.

  • The core decomposition of networks: theory, algorithms and applications
    VLDB J. (IF 1.973) Pub Date : 2019-11-04
    Fragkiskos D. Malliaros, Christos Giatsidis, Apostolos N. Papadopoulos, Michalis Vazirgiannis

    Abstract The core decomposition of networks has attracted significant attention due to its numerous applications in real-life problems. Simply stated, the core decomposition of a network (graph) assigns to each graph node v, an integer number c(v) (the core number), capturing how well v is connected with respect to its neighbors. This concept is strongly related to the concept of graph degeneracy, which has a long history in graph theory. Although the core decomposition concept is extremely simple, there is an enormous interest in the topic from diverse application domains, mainly because it can be used to analyze a network in a simple and concise manner by quantifying the significance of graph nodes. Therefore, there exists a respectable number of research works that either propose efficient algorithmic techniques under different settings and graph types or apply the concept to another problem or scientific area. Based on this large interest in the topic, in this survey, we perform an in-depth discussion of core decomposition, focusing mainly on: (i) the basic theory and fundamental concepts, (ii) the algorithmic techniques proposed for computing it efficiently under different settings, and (iii) the applications that can benefit significantly from it.

  • On Differentially Private Frequent Itemset Mining.
    VLDB J. (IF 1.973) Pub Date : 2013-09-17
    Chen Zeng,Jeffrey F Naughton,Jin-Yi Cai

    We consider differentially private frequent itemset mining. We begin by exploring the theoretical difficulty of simultaneously providing good utility and good privacy in this task. While our analysis proves that in general this is very difficult, it leaves a glimmer of hope in that our proof of difficulty relies on the existence of long transactions (that is, transactions containing many items). Accordingly, we investigate an approach that begins by truncating long transactions, trading off errors introduced by the truncation with those introduced by the noise added to guarantee privacy. Experimental results over standard benchmark databases show that truncating is indeed effective. Our algorithm solves the "classical" frequent itemset mining problem, in which the goal is to find all itemsets whose support exceeds a threshold. Related work has proposed differentially private algorithms for the top-k itemset mining problem ("find the k most frequent itemsets".) An experimental comparison with those algorithms show that our algorithm achieves better F-score unless k is small.

  • Adding data provenance support to Apache Spark.
    VLDB J. (IF 1.973) Pub Date : 2019-04-23
    Matteo Interlandi,Ari Ekmekji,Kshitij Shah,Muhammad Ali Gulzar,Sai Deep Tetali,Miryung Kim,Todd Millstein,Tyson Condie

    Debugging data processing logic in data-intensive scalable computing (DISC) systems is a difficult and time-consuming effort. Today's DISC systems offer very little tooling for debugging programs, and as a result, programmers spend countless hours collecting evidence (e.g., from log files) and performing trial-and-error debugging. To aid this effort, we built Titian, a library that enables data provenance-tracking data through transformations-in Apache Spark. Data scientists using the Titian Spark extension will be able to quickly identify the input data at the root cause of a potential bug or outlier result. Titian is built directly into the Spark platform and offers data provenance support at interactive speeds-orders of magnitude faster than alternative solutions-while minimally impacting Spark job performance; observed overheads for capturing data lineage rarely exceed 30% above the baseline job execution time.

  • Performance analysis of a dual-tree algorithm for computing spatial distance histograms.
    VLDB J. (IF 1.973) Pub Date : 2011-08-02
    Shaoping Chen,Yi-Cheng Tu,Yuni Xia

    Many scientific and engineering fields produce large volume of spatiotemporal data. The storage, retrieval, and analysis of such data impose great challenges to database systems design. Analysis of scientific spatiotemporal data often involves computing functions of all point-to-point interactions. One such analytics, the Spatial Distance Histogram (SDH), is of vital importance to scientific discovery. Recently, algorithms for efficient SDH processing in large-scale scientific databases have been proposed. These algorithms adopt a recursive tree-traversing strategy to process point-to-point distances in the visited tree nodes in batches, thus require less time when compared to the brute-force approach where all pairwise distances have to be computed. Despite the promising experimental results, the complexity of such algorithms has not been thoroughly studied. In this paper, we present an analysis of such algorithms based on a geometric modeling approach. The main technique is to transform the analysis of point counts into a problem of quantifying the area of regions where pairwise distances can be processed in batches by the algorithm. From the analysis, we conclude that the number of pairwise distances that are left to be processed decreases exponentially with more levels of the tree visited. This leads to the proof of a time complexity lower than the quadratic time needed for a brute-force algorithm and builds the foundation for a constant-time approximate algorithm. Our model is also general in that it works for a wide range of point spatial distributions, histogram types, and space-partitioning options in building the tree.

Contents have been reproduced by permission of the publishers.
上海纽约大学William Glover