Proposed Data Structure Research Articles

Routing on large scale mobile ad hoc networks using bloom filters

A bloom filter is a probabilistic data structure used to test whether an element is a member of a set. The bloom filter shares some similarities to a standard hash table but has a higher storage efficiency. As a drawback, bloom filters allow the existence of false positives. These properties make bloom filters a suitable candidate for storing topological information in large-scale mobile ad hoc networks, where there is a considerable amount of data to be exchanged. Bloom filters enable the transmission of reduced routing control messages to save available bandwidth, and they require fewer node resources than traditional data structures. Existing ad hoc routing protocols using bloom filters limit themselves to static sensor networks or small/medium-scale mobile networks. In this study, we propose and analyse a routing protocol suited for large scale mobile ad hoc networks (up to 3000 nodes) that stores and disseminates topological information through a specific type of bloom filter that is able to discard old elements. Logical overlays are then constructed with the proposed data structures to indicate the distance to the destination nodes. This process allows the routing protocol to reduce the number of control messages required to discover and maintain routes. The proposed algorithm is validated via simulation and compared with other well-known routing protocols developed for mobile ad hoc networks.

Improved Data Structure for the Orthogonal Packing Problem

In paper is considered the actual in industry and engineering orthogonal multidimensional packing problem. This problem is NP-hard in strong sense therefore an important role is played effectiveness of the used packing representation model. To increase the speed of placement of a given set of orthogonal objects into containers is offered a new data structure – multilevel linked data structure. The carried out computational experiments demonstrate high time efficiency of the proposed data structure compared to the ordered simple linked list.

Reducing the bottleneck of graph-based data mining by improving the efficiency of labeled graph isomorphism testing

Due to the complex nature of graph representations, the isomorphism testing between a pair of labeled graphs becomes one of the most time-consuming procedures during the process of graph-based data mining. In order to reduce this bottleneck, in this paper we propose a novel efficient algorithm to perform isomorphism testing of labeled graphs which in general performs less state-space tree searching. The proposed method uses graph signatures as the first-step filter, and it limits the backtracking occurring only between each pair of corresponding vertex classes, based on the proposed data structures and the vertex partition method. We compared the proposed method with state-of-the-art methods to verify its efficiency for several datasets each with different aspects of characteristics. The experimental results show that for irregular graphs, either labeled or unlabeled, the proposed method outperforms the compared methods in efficiency. For graphs with multiple labels but high regularity, the proposed method is still better than the compared methods. The result of this algorithm is directly applicable to those emerging applications related to graph-based data mining which need to perform isomorphism testing of labeled graphs in large databases.

Using cascading Bloom filters to improve the memory usage for de Brujin graphs

BackgroundDe Brujin graphs are widely used in bioinformatics for processing next-generation sequencing data. Due to a very large size of NGS datasets, it is essential to represent de Bruijn graphs compactly, and several approaches to this problem have been proposed recently.ResultsIn this work, we show how to reduce the memory required by the data structure of Chikhi and Rizk (WABI’12) that represents de Brujin graphs using Bloom filters. Our method requires 30% to 40% less memory with respect to their method, with insignificant impact on construction time. At the same time, our experiments showed a better query time compared to the method of Chikhi and Rizk.ConclusionThe proposed data structure constitutes, to our knowledge, currently the most efficient practical representation of de Bruijn graphs.

A Retrieval Method for Double Array Structures by Using Byte N-Gram

III describes the proposed data structures and retrieval algorithms. Experimental evaluations are given in Section IV. Finally, Section V concludes the proposed algorithm and describes further works.

High utility itemset mining with techniques for reducing overestimated utilities and pruning candidates

High utility itemset mining considers the importance of items such as profit and item quantities in transactions. Recently, mining high utility itemsets has emerged as one of the most significant research issues due to a huge range of real world applications such as retail market data analysis and stock market prediction. Although many relevant algorithms have been proposed in recent years, they incur the problem of generating a large number of candidate itemsets, which degrade mining performance. In this paper, we propose an algorithm named MU-Growth (Maximum Utility Growth) with two techniques for pruning candidates effectively in mining process. Moreover, we suggest a tree structure, named MIQ-Tree (Maximum Item Quantity Tree), which captures database information with a single-pass. The proposed data structure is restructured for reducing overestimated utilities. Performance evaluation shows that MU-Growth not only decreases the number of candidates but also outperforms state-of-the-art tree-based algorithms with overestimated methods in terms of runtime with a similar memory usage.

BITS-Tree -- An Efficient Data Structure for Segment Storage and Query Processing

In this paper, a new and novel data structure is proposed to dynamically insert and delete segments. Unlike the standard segment trees, the proposed data structure permits insertion of a segment with interval range beyond the interval range of the existing tree, which is the interval between minimum and maximum values of the end points of all the segments. Moreover, the number of nodes in the proposed tree is lesser as compared to the dynamic version of the standard segment trees, and is able to answer both stabbing and range queries practically much faster compared to the standard segment trees.

STEP-NC compliant approach for setup planning problem on multiple fixture pallets

Manufacturing system configuration is a broad problem that involves various topics concerning workpiece, cutters, fixture and machine. This paper presents an approach to solve the setup planning problem on machining centres based on a STEP-NC compliant data structure. The aim of this approach is to shorten the time required for the process planning activity by automating some time-consuming steps without compromising the solution accuracy. In the proposed approach, a CAM software tool is employed to associate the geometric and technological data regarding the product. Using the proposed data structure, a method is proposed for solving the setup planning problem based on mathematical programming. The developed approach has been tested on a real case.

UT-Tree: Efficient mining of high utility itemsets from data streams

High utility itemsets mining is a hot topic in data stream mining. It is essential that the mining algorithm should be efficient in both time and space for data stream is continuous and unbounded. To the best of our knowledge, the existing algorithms require multiple database scans to mine high utility itemsets, and this hinders their efficiency. In this paper, we propose a new data structure, called UT-Tree Utility on Tail Tree, for maintaining utility information of transaction itemsets to avoid multiple database scans. The UT-Tree is created with one database scan, and contains a fixed number of transaction itemsets; utility information is stored on tail-nodes only. Based on the proposed data structure and the sliding window approach, we propose a mining algorithm, called HUM-UT High Utility itemsets Mining based on UT-Tree, to find high utility itemsets from transactional data streams. The HUM-UT algorithm mines high utility itemsets from the UT-Tree without additional database scan. Experiment results show that our algorithm has better performance and is more stable under different experimental conditions than the state-of-the-art algorithm HUPMS in terms of time and space.

Spatiotemporal indexing for moving objects in an indoor cellular space

To facilitate a variety of indoor applications, positioning technologies have been developed in indoor spaces (such as WI-FI and RFID). Thus, the requirement for the tracking and monitoring of moving objects in indoor spaces has increased considerably. The indexing of moving objects in indoor spaces is of essential importance, as these are different from outdoor spaces in many respects, such as the measurements and the positioning technologies. Therefore, in this paper, we propose a new adjacency-index structure for objects moving in indoor space which includes both spatial and temporal properties. The spatial index is based on the connectivity (adjacency) between the indoor environment cells. Moreover, we propose two temporal indexes with different methods to store the temporal data, which can support and enable efficient query processing and efficient updates for objects moving in indoor space. The proposed indexes can efficiently serve different types of spatial queries, such as KNN and indoor range, and a variety of temporal queries which are essential in an indoor environment. An empirical performance study suggests that the proposed data structures are effective, efficient, and robust.

N-step FM-Index for Faster Pattern Matching

Fast pattern matching is a requirement for many problems, specially for bioinformatics sequence analysis like short read mapping applications. This work presents a variation of the FM-index method, denoted n-step FM-index, that is applied in exact match genome search.We propose an alternative two-dimensional FM-index structure that allows backward-search navigation giving steps of n symbols at a time. The main advantages of this arrangement are the reduction of the computational work, but most importantly, the reduction by n of the chain of dependent data accesses, and the increase in the temporal locality of the data access pattern. This benefit comes at the expense of increasing the total amount of data required for the index.We present an in-depth performance analysis of a multi-core implementation of the algorithm using large references (up to 1.5G). We identify memory latency as the major performance limiter for single-thread execution and memory bandwidth for multi-thread execution. Our proposal provides speedups ranging from 1.4× to 2.4×, when there is no limitation on DRAM capacity.We also analyse the trade-off of compacting the proposed data structure in order to reduce memory capacity requirements, now at the expense of increasing execution time. An extra 33% of DRAM space allows our proposal to improve performance by 1.2×, while doubling DRAM size enables an additional 1.5×.Our proposal of n-step algorithm provides an alternative for pseudo-random memory access algorithms to be redesigned to scale in current and future computer systems.

A UNIFIED FRAMEWORK FOR BI(TRI)CONNECTIVITY AND CHORDAL AUGMENTATION

For a connected non-complete graph, a vertex separator is a subset of vertices whose deletion increases the number of connected components and the vertex connectivity of the graph refers to the size of a minimum vertex separator. A graph with the vertex connectivity k is said to be k-vertex connected. Given a k-vertex connected graph G, vertex connectivity augmentation determines a smallest set of edges whose augmentation to G makes it (k + 1)-vertex connected. In this paper, we report our study on connectivity augmentation in 1-connected graphs, 2-connected graphs, and k-connected chordal graphs. We first represent the graph under consideration using a "tree-like" graph. This tree is unique and explicitly captures the connectivity information of the graph. Using this tree, our proposed data structure maintains the set of equivalence classes based on an equivalence relation on the set of leaves of the tree. This partition determines a set of edges to be augmented to increase the connectivity of the graph by one. Based on our data structure, we present a new combinatorial analysis and an elegant proof of correctness of our linear-time algorithm for an optimum connectivity augmentation. The novelty is in the data structure which is a unified framework for all three augmentations. As far as the run-time analysis is concerned, given the associated tree, our approach yields an augmentation set in linear time.

A B-Rep data structure and object GUI programming to implement 2D boundary elements

One of the main advantages of the Boundary Elements Methods (BEM) is the inherent feature of a Boundary Representation scheme (B-Rep),since it represents the geometry and physical properties of a model using just its boundary. This characteristic makes the representation andmanipulation of the model geometry an easy task, also allowing the boundary element mesh generation in a straightforward and simple way.Even though a simple pre-processor program may suffice for generating most two-dimensional boundary element models, in some specialcases the modeling procedure represents a cumbersome task, requiring pre-processor programs with enhanced characteristics. The specialcases considered in this work are non-trivial models, such as geometries with infinite or semi-infinite domains, multiple regions, disconnectedregions, cracks and holes. To handle with the modeling task of these special models, the present development proposes a scheme based onthe well-known Half-Edge data structure (HED). It also considers the use of Object-Oriented Programming (OOP) to implement a graphicaluser interface (GUI) with the proposed data structure. The results of step-by-step generation of some special two-dimensional models in theimplemented GUI illustrate the main characteristics of the proposed scheme.Keywords: Geometric Modeling, HED Data Structure, BEM, OOP, GUIs.

고체 추진기관 구조체 설계 프로그램 개발을 위한 설계 부품 자료 구조

In this paper, we proposed a data structure to represent structural components of solid rocket motors (SRM) in an automated design program. To propose the data structure, we searched the necessary functions for the automated design program should have. In order to design the structural components of solid rocket motors sufficiently with a design program, it should have the functions to represent the shapes of the components, the drawing and analysis models, the design variables, various product structures, interferences, characteristic properties, design equations, and tightening sets. By modifying the data structure of an element object that is a general purpose data structure to represent a general component of a product, a new data structure was proposed to satisfy all the necessary functions with optimum. Finally, a design program for the structural components of solid rocket motors was developed successfully with the proposed data structure.

A Classified Multisuffix Trie for IP Lookup and Update

In this paper, a new data structure, called the classified multisuffix trie (CMST), is proposed for designing dynamic router-tables. CMST achieves a better performance than existing data structures because each node can store more than one prefix and the longest matching prefix may be found in an internal node rather than on a leaf. Furthermore, with the classification in each node, the dynamic router-table operations can be performed efficiently. To reduce the memory requirement, we store each prefix's corresponding suffix in a CMST node, instead of storing a full binary string. Based on the CMST, we also propose another data structure, called the Partitioning Classified Multisuffix Trie (PCMST) to reduce the height of the trie and expedite router-table operations. Experiments using real IPv4 routing databases demonstrate that the proposed data structures are efficient in terms of memory usage and it performs well in terms of the average times of the lookup, insert, and delete operations. We report the results of experiments conducted to compare the performance of the proposed data structure with that of other structures using the benchmark IPv4 prefix databases AS4637, AS6447, and AS65000 with 219,581, 296,552, and 226,847 prefixes, respectively.

Implementing a Novel Data Structure for Maintaining Cumulative Frequency of Symbols

A new data structure, namely “cumulative frequency matrix (CFM)”, is proposed here for maintaining cumulative frequencies. For an order-0 model having 256 symbols, CFM is a 2-D array of 16 rows and 16 columns. Two nibbles, say L for left and R for right, of a byte symbol represents row and column dimensions respectively. Matrix element (L, R) represents cumulative frequency of symbol with right nibble as R among symbols with left nibble as L. Within row, it stores cumulative frequency of symbols with right nibble varying from 0 to 15. Adaptive arithmetic coding is a lossless data compression method. It needs to update cumulative frequencies at runtime. Various algorithms for maintaining cumulative frequencies, computing cumulative frequency interval etc. are discussed here. Practical implementation shows that proposed data structure is simpler as well as efficient as compared to other data structures in use. General Terms Data Compression, Data Structures, Algorithms, Adaptive Arithmetic Coding, Cumulative Frequencies

Parallel SAH Based KD Tree Construction Algorithm

The best method to construct a high quality KD tree is by using Surface Area Heuristic[1][4] (SAH). However, the computation involved in SAH is expensive. Wald and Havran[2] introduced a sequential algorithm of O(NlogN) complexity, which reached the lower bound of constructing a binary tree. In this paper, we will introduce a parallel SAH KD tree construction algorithm based on the O(NlogN) sequential algorithm. We proposed data structure for parallel constructing process which can minimize the communication among working threads, and a load balance strategy for evenly distributing work load. Our algorithm can achieve 4x speed up on 8-core CPUs architecture.

Arboricity, [formula omitted]-index, and dynamic algorithms

We propose a new data structure for manipulating graphs, called h-graph, which is particularly suited for designing dynamic algorithms. The structure itself is simple, consisting basically of a triple of elements, for each vertex of the graph. The overall size of all triples is O(n+m), for a graph with n vertices and m edges. We describe algorithms for performing the basic operations related to dynamic applications, as insertions and deletions of vertices or edges, and adjacency queries. The data structure employs a technique first described by Chiba and Nishizeki [Chiba, Nishizeki, Arboricity and subgraph listing algorithms, SIAM J. Comput. 14 (1) (1985) 210–223], and relies on the arboricity of graphs. Using the proposed data structure, we describe several dynamic algorithms for solving problems as listing the cliques of a given size, recognizing diamond-free graphs, and finding simple, simplicial and dominated vertices. These algorithms are the first of their kind to be proposed in the literature. In fact, the dynamic algorithms for the above problems lead directly to new static algorithms, and using the data structure we also design new static algorithms for the problems of counting subgraphs of size 4, recognizing cop-win graphs and recognizing strongly chordal graphs. The complexities of all of the proposed static algorithms improve over the complexities of the so far existing algorithms, for graphs of low arboricity. In addition, for the problems of counting subgraphs of size 4 and recognizing diamond-free graphs, the improvement is general.

On-demand time-decaying bloom filters for telemarketer detection

Several traffic monitoring applications may benefit from the availability of efficient mechanisms for approximately tracking smoothed time averages rather than raw counts. This paper provides two contributions in this direction. First, our analysis of Time-decaying Bloom filters, formerly proposed data structures devised to perform approximate Exponentially Weighted Moving Averages on streaming data, reveals two major shortcomings: biased estimation when measurements are read in arbitrary time instants, and slow operation resulting from the need to periodically update all the filter's counters at once. We thus propose a new construction, called On-demand Time-decaying Bloom filter, which relies on a continuous-time operation to overcome the accuracy/performance limitations of the original window-based approach. Second, we show how this new technique can be exploited in thedesign of high performance stream-based monitoring applications, by developing VoIPSTREAM, a proof-of-concept real-time analysis version of a formerly proposed system for telemarketing call detection. Our validation results, carried out over real telephony data, show how VoIPSTREAM closely mimics the feature extraction process and traffic analysis techniques implemented in the offline system, at a significantly higher processing speed, and without requiring any storage of per-user call detail records.

Multiprefix Trie: A New Data Structure for Designing Dynamic Router-Tables

IP lookup affects the speed of an incoming packet and the time required to determine which output port the packet should be sent to; hence, it plays an important role in the design of router-tables. In this paper, we propose a new data structure, called a multiprefix trie, for use in designing dynamic router-tables. One key feature of our data structure is that each node can store more than one prefix, which reduces the number of memory accesses. When performing lookup, the structure can search more prefixes in one node and may find the longest matching prefix in an internal node rather than on a leaf. Moreover, when updating the router-table, it does not need to reconstruct the table. As a by-product, the proposed data structure minimizes the time required for dynamic router-table operations, including lookup, insertion, and deletion, and also reduces the number of memory accesses. We report the results of experiments conducted to compare the proposed data structure with other structures using the benchmark IPv4 prefix database AS4637 with 219,581 prefixes.