Query Operations Research Articles

The Influence of Digital Surface Model Choice on Visibility‐based Mobile Geospatial Applications

AbstractIn this work we investigate the effectiveness of different types of visibility models for use within location‐based services. This article outlines the methodology and results for our experiments, which were designed to understand the accuracy and effects of model choices for mobile visibility querying. Harnessing a novel mobile media consumption and authoring application called Zapp, the levels of accuracy of various digital surface representations used by a line of sight visibility algorithm are extensively examined by statistically assessing randomly sampled viewing sites across the 1 km2 study area, in relation to points of interest (POI) across the University of Nottingham campus. Testing was carried out on three different surface models derived from 0.5 m LiDAR data by visiting physical sites on each surface model with 14 random point of interest masks being viewed from between 10 and 16 different locations, totalling 190 data points. Each site was ground‐truthed by determining whether a given POI could be seen by the user and could also be identified by the mobile device. Our experiments in a semi‐urban area show that choice of surface model has important implications for mobile applications that utilize visibility in geospatial query operations.

In-Network Processing of Joins in Wireless Sensor Networks

The join or correlated filtering of sensor readings is one of the fundamental query operations in wireless sensor networks (WSNs). Although the join in centralized or distributed databases is a well-researched problem, join processing in WSNs has quite different characteristics and is much more difficult to perform due to the lack of statistics on sensor readings and the resource constraints of sensor nodes. Since data transmission is orders of magnitude more costly than processing at a sensor node, in-network processing of joins is essential. In this paper, the state-of-the-art techniques for join implementation in WSNs are surveyed. The requirements and challenges, join types, and components of join implementation are described. The open issues for further research are identified.

Automatically reasoning about metamodeling

Metamodeling is foundational to many modeling frameworks, and so it is important to formalize and reason about it. Ideally, correctness proofs and test-case generation on the metamodeling framework should be automatic. However, it has yet to be shown that extensive automated reasoning on metamodeling frameworks can be achieved. In this paper, we present one approach to this problem: metamodeling frameworks are specified modularly using algebraic data types and constraint logic programming (CLP). Proofs and test-case generation are encoded as open world query operations and automatically solved.

Aggregate nearest neighbor queries in uncertain graphs

Most recently, uncertain graph data begin attracting significant interests of database research community, because uncertainty is the intrinsic property of the real-world and data are more suitable to be modeled as graphs in numbers of applications, e.g. social network analysis, PPI networks in biology, and road network monitoring. Meanwhile, as one of the basic query operators, aggregate nearest neighbor (ANN) query retrieves a data entity whose aggregate distance, e.g. sum, max, to the given query data entities is smaller than those of other data entities in a database. ANN query on both certain graph data and high dimensional data has been well studied by previous work. However, existing ANN query processing approaches cannot handle the situation of uncertain graphs, because topological structures of an uncertain graph may vary in different possible worlds. Motivated by this, we propose the aggregate nearest neighbor query in uncertain graphs (UG-ANN) in this paper. First of all, we give the formal definition of UG-ANN query and the basic UG-ANN query algorithm. After that, to improve the efficiency of UG-ANN query processing, we develop two kinds of pruning approaches, i.e. structural pruning and instance pruning. The structural pruning takes advantages the monotonicity of the aggregate distance to derive the upper and lower bounds of the aggregate distance for reducing the graph size. Whereas, the instance pruning decreases the number of possible worlds to be checked in the searching tree. Comprehensive experimental results on real-world data sets demonstrate that the proposed method significantly improves the efficiency of the UG-ANN query processing.

Distributed Mapping Management of Identifiers and Locators in LISP-based Mobile Networks

The Locator Identifier Separation Protocol (LISP) has been proposed as an identifier-locator separation scheme for scalable Internet routing. However, LISP was originally designed in the fixed network environment rather than in the mobile network environment. In particular, the existing LISP mobility schemes are based on a centralized map server that is used as an anchor point for mobile nodes, and thus intrinsically subject to some limitations in mobile environment. In this paper, we propose a distributed mapping management of Endpoint Identifiers (EIDs) and Locators (LOCs) in mobile LISP networks. We use Routing LOC (RLOC) and Local LOC (LLOC) as locators for mobile hosts. RLOC represents the IP address of the domain gateway, and LLOC is the IP address of the access router that a host is currently attached to. For EID-LOC mapping management, each network domain has a Distributed Map Server (DMS) over its gateway. Each DMS keeps track of the EID-LOC mapping information for mobile hosts in the distributed way. The proposed scheme is also a network-based approach, in which each access router, instead of a host, performs the mapping management operations. From the performance analysis, we can see that the proposed distributed scheme can give better performance than the existing schemes in terms of the signaling delays required for EID-LOC mapping update and query operations.

Skyline Query for Selecting Spatial Objects by Utilizing Surrounding Objects

With the increase of data volume, advanced query operators such as skyline queries are necessary in order to help users to handle the huge amount of available data by selecting a set of promising data objects. In this paper, we present a method for selecting spatial objects, such as houses, based on the objects of the surrounding facilities such as restaurants, supermarkets, and stations. In our approach, at first a user specifies a list of surrounding facilities within a specified distance in his preferred location. Our system then computes a set of spatial objects in the preferred location considering the objects of the surrounding facilities by utilizing the idea of skyline queries. We perform different experiments to show the effectiveness of our approach. Experimental evaluation shows that our approach is well applicable for efficient decision making.

H-HIBASE: Compression Enhancement of HIBASE Technique Using Huffman Coding

HIBASE compression technique simply replaces the attribute values in a tuple with fixed length code-words. However, fixed length coding system is not an optimal compression technique because some redundancies occur in the compressed table. This redundancy can be avoided if we use Huffman code-words. Moreover, using Huffman code-word will ensure optimal compression as well as high performance operation. The objectives of the research are to i) develop a dictionary by applying the principle of Huffman coding, ii) compress the relational storage of HIBASE by applying dynamic Huffman coding, iii) develop algorithm to perform query operation on the compressed storage, iv) analyze the performance of the proposed system in terms of both storage and queries. The main contribution of this research is to develop a compression technique. It implies the enhancement of HIBASE technique using HUFFMAN coding (H-HIBASE) with better compression capability.

A Heaviest Hitters Limiting Mechanism with O(1) Time Complexity for Sliding-Window Data Streams

In this work we address the problem of identifying and limiting the heaviest hitters in a sliding-window data stream. We propose the first, to our knowledge, exact (i.e., not approximate) algorithm which achieves O(1) with high probability time complexity in both update and query operations. Additionally, it tracks the first and last item of any itemset in the window in O(1) time complexity as well as the lightest hitters with no additional computational costs. These properties allow us to efficiently implement a mechanism to limit the heaviest hitters by evicting them from or not allowing them in the window. We describe the algorithms and data structure which implement this functionality, we explain how they can be used to accomplish the goal of limiting the heaviest hitters and perform experiments to produce quantitative results to support our theoretical arguments.

MataNui–A Distributed Storage Infrastructure for Scientific Data

In science and engineering the problem of «sanity» in data management is quite common. Particularly, if partners within a project are geographically distributed, and require access to this data. These partners would ideally like to access or store data using a local server, while still retaining remote access. Furthermore, data does not «live alone,» but has a «partner»: Meta- data. Currently available solutions cater for different needs, and do not fully address the problems in our own environment. Particularly, seamless integration within a Grid infrastructure is essential.For these reasons, the presented MataNui data server infrastructure offers full storage of files along with arbitrarily exten- sive meta-data for each stored item. The server can replicate its content between geographically distributed locations, making each usable for full read/write access, enabling researchers to use the one closest to their own network. Files are accessible through GridFTP as the predominant protocol for Grid Computing. For full access to meta-data and server side search queries a (RESTful) Web Service is provided. The current implementation features very high performance in data throughput on storage, retrieval and query operations. Through the Web Service front end it is straight forward to integrate it with scientific workflow environments or scientific data management applications.

Skewness‐aware clustering tree for unevenly distributed spatial sensor nodes in smart city

SUMMARYEfficient spatial index is essential for querying spatial sensor nodes in the context of smart city. Sensor nodes are usually unevenly distributed in real situations. In this setting, R‐tree and its variants may cause large overlap and coverage among branch nodes, which impact the query efficiency greatly. To address this challenge, this paper proposes a novel skewness‐aware clustering tree (SWC‐tree) by clustering sensor nodes. Sensor nodes in a dense region will be put into the same node. Thus, overlap and coverage among node regions are less than that of R‐tree and its variants. As dense regions contain more sensor nodes, we assign a higher priority to these region nodes for facilitating the query operation. Experimental results show that in the context of skewed distribution, SWC‐tree is efficient in performance for conducting insertion, deletion, and query operations of sensor nodes. Copyright © 2012 John Wiley & Sons, Ltd.

An in-place min–max priority search tree

One of the classic data structures for storing point sets in R2 is the priority search tree, introduced by McCreight in 1985. We show that this data structure can be made in-place, i.e., it can be stored in an array such that each entry stores only one point of the point set and no entry is stored in more than one location of that array. It combines a binary search tree with a heap. We show that all the standard query operations can be performed within the same time bounds as for the original priority search tree, while using only O(1) extra space.We introduce the min–max priority search tree which is a combination of a binary search tree and a min–max heap. We show that all the standard queries which can be done in two separate versions of a priority search tree can be done with a single min–max priority search tree.As an application, we present an in-place algorithm to enumerate all maximal empty axis-parallel rectangles amongst points in a rectangular region R in R2 in O(mlogn) time with O(1) extra space, where m is the total number of maximal empty rectangles.

Parallel pipelined filter ordering with precedence constraints

In the parallel pipelined filter ordering problem, we are given a set of n filters that run in parallel. The filters need to be applied to a stream of elements, to determine which elements pass all filters. Each filter has a rate limit r i on the number of elements it can process per unit time, and a selectivity p i , which is the probability that a random element will pass the filter. The goal is to maximize throughput. This problem appears naturally in a variety of settings, including parallel query optimization in databases and query processing over Web services. We present an O ( n 3 ) algorithm for this problem, given tree-structured precedence constraints on the filters. This extends work of Condon et al. [2009] and Kodialam [2001], who presented algorithms for solving the problem without precedence constraints. Our algorithm is combinatorial and produces a sparse solution. Motivated by join operators in database queries, we also give algorithms for versions of the problem in which “filter” selectivities may be greater than or equal to 1. We prove a strong connection between the more classical problem of minimizing total work in sequential filter ordering (A), and the parallel pipelined filter ordering problem (B). More precisely, we prove that A is solvable in polynomial time for a given class of precedence constraints if and only if B is as well. This equivalence allows us to show that B is NP-Hard in the presence of arbitrary precedence constraints (since A is known to be NP-Hard in that setting).

A generic data model for moving objects

Moving objects databases should be able to manage trips that pass through several real world environments, e.g., road network, indoor. However, the current data models only deal with the movement in one situation and cannot represent comprehensive trips for humans who can move inside a building, walk on the pavement, drive on the road, take the public vehicles (bus or train), etc. As a result, existing queries are solely limited to one environment. In this paper, we design a data model that is able to represent moving objects in multiple environments in order to support novel queries on trips in different surroundings and various transportation modes (e.g., Car, Walk, Bus). A generic and precise location representation is proposed that can apply in all environments. The idea is to let the space for moving objects be covered by a set of so-called infrastructures each of which corresponds to an environment and defines the available places for moving objects. Then, the location is represented by referencing to the infrastructure. We formulate the concept of space and infrastructure and propose the methodology to represent moving objects in different environments with the integration of precise transportation modes. Due to different infrastructure characteristics, a set of novel data types is defined to represent infrastructure components. To efficiently support new queries, we design a group of operators to access the data. We present how such a data model is implemented in a database system and report the experimental results. The new model is designed with attention to the data models of previous work for free space and road networks to have a consistent type system and framework of operators. In this way, a powerful set of generic query operations is available for querying, together with those dealing with infrastructures and transportation modes. We demonstrate these capabilities by formulating a set of sophisticated queries across all infrastructures.

Distributed top-k query processing by exploiting skyline summaries

Recently, a trend has been observed towards supporting rank-aware query operators, such as top-k, that enable users to retrieve only a limited set of the most interesting data objects. As data nowadays is commonly stored distributed over multiple servers, a challenging problem is to support rank-aware queries in distributed environments. In this paper, we propose a novel approach, called DiTo, for efficient top-k processing over multiple servers, where each server stores autonomously a fraction of the data. Towards this goal, we exploit the inherent relationship of top-k and skyline objects, and we employ the skyline objects of servers as a data summarization mechanism for efficiently identifying the servers that store top-k results. Relying on a thresholding scheme, DiTo retrieves the top-k result set progressively, while the number of queried servers and transferred data is minimized. Furthermore, we extend DiTo to support data summarizations of bounded size, thus restricting the cost of summary distribution and maintenance. To this end, we study the challenging problem of finding an abstraction of the skyline set of fixed size that influences the performance of DiTo only slightly. Our experimental evaluation shows that DiTo performs efficiently and provides a viable solution when a high degree of distribution is required.

DBToaster

Applications ranging from algorithmic trading to scientific data analysis require realtime analytics based on views over databases that change at very high rates. Such views have to be kept fresh at low maintenance cost and latencies. At the same time, these views have to support classical SQL, rather than window semantics, to enable applications that combine current with aged or historical data. In this paper, we present viewlet transforms , a recursive finite differencing technique applied to queries. The viewlet transform materializes a query and a set of its higher-order deltas as views. These views support each other's incremental maintenance, leading to a reduced overall view maintenance cost. The viewlet transform of a query admits efficient evaluation, the elimination of certain expensive query operations, and aggressive parallelization. We develop viewlet transforms into a workable query execution technique, present a heuristic and cost-based optimization framework, and report on experiments with a prototype dynamic data management system that combines viewlet transforms with an optimizing compilation technique. The system supports tens of thousands of complete view refreshes a second for a wide range of queries.

Research of Query Optimization Based on Improved Quantum Particle Swarm Optimization Algorithm in Distributed Database

Database query optimization is a very complicated issue, also is the key influencing factor in database systems performance. Database query operation efficiency is one of the key factors that affect system response time. Therefore, how to improve the efficiency of database query system becomes particularly important. This paper, on the basis of the advantages of Quantum particle swarm optimization algorithm, proposes distributed database query optimization methods based on Quantum particle swarm optimization algorithm, and improves algorithm. Simulation comparison experiments show that Quantum particle swarm optimization algorithm can improve the efficiency of the distributed database query, and is an effective way to solve the optimization of distributed database query.

Numeric-based XML labeling schema by generalized dynamic method

Most efficient indeces and query techniques over XML (extensible markup language) data are based on a certain labeling scheme, which can quickly determine ancestor-descendant and parent-child relationship between two nodes. The current basic labeling schemes such as containment scheme and prefix scheme cannot avoid relabeling when XML documents are updated. After analyzing the essence of existing dynamic XML labels such as compact dynamic binary string (CDBS) and vector encoding, this paper gives a common unifying framework for the numeric-based generalized dynamic label, which can be implemented into a variety of dynamic labels according to the different user-defined value comparison methods. This paper also proposes a novel dynamic labeling scheme called radical sign label. Extensive experiments show that the radical sign label performs well for the initialization, insertion and query operations, and especially for skewed insertion where the storage cost of the radical sign label is better than that of former methods.

Update strategy based on region classification using ELM for mobile object index

Mobile object index should support efficient update operations besides efficient query operations. In this paper, we consider the issue of the efficient updating of mobile object index. Based on a model for the mobile data, we introduce a method of incorporating statistical information of the regions covered by the mobile objects into feature vectors. We then propose a novel architecture of mobile object index, where R-tree is used to index the occupied regions instead of the mobile objects themselves and extreme learning machine (ELM) is used to classify the regions. Further, we describe several related algorithms and the update strategy based on the classification of the regions. The proposed strategy and algorithms are evaluated in a simulated environment. The experiments demonstrate that the proposed update strategy based on region classification using ELM can achieve higher performance with respect to I/O operations. Compared to the strategy without region classification, the proposed method can reduce the number of I/O operations more than 80%.

A Performance Evaluation of Preference Evaluation Techniques in Real High Dimensional Database

Preference query has received high interest due to its great benefits over various types of database applications. This type of query provides more flexible query operators that retrieve data items which are not dominated by the other data items in all attributes (dimensions). Many preference techniques for preference queries have been introduced including top-k, skyline, multi-objective skyline, top-k dominating, k-dominance, ranked skyline, and k-frequency. All of these preference techniques aimed at finding the “best” result that meets the user preferences. This paper aims at evaluating the performance of the five well-known preference evaluation techniques, namely: top-k, skyline, top-k dominating, k-dominance and k-frequency; in a real database application when high number of dimensions is the main concern. To achieve this, a recipe searching application with maximum number of 60 dimensions has been developed which assists users to identify the most desired recipes that fulfill their preferences. Several analyses have been carried out, where execution time is the main measurement used to evaluate each preference technique.

The Research of Set Code and Related Algorithms

In the traditional process of set operations, the elements of set are usually represented through natural language rather than formalized representation, which affected the efficiency of set operations. In order to solve this problem and improve the efficiency of set operations, this paper innovatively introduced the binary into the process of set operations, proposed the concept of “set code”, and defined a series of operation rules about “set code”. All the above formed a relatively complete and formal computing system of “set code”. Experimental results show that: the process of set operations can be achieved through 0, 1 operation in the form of “set code”. We also successfully achieved the target of query operation which is very important in the database through the mechanism of “set code”.