Special Data Structure Research Articles

Analysis of tweets to find the basis of popularity based on events semantic similarity

PurposeThe purpose of this paper is to propose and experiment a framework for analysing the tweets to find the basis of popularity of a person and extract the reasons supporting the popularity. Although the problem of analysing tweets to detect popular events and trends has recently attracted extensive research efforts, not much emphasis has been given to find out the reasons behind the popularity of a person based on tweets.Design/methodology/approachIn this paper, the authors introduce a framework to find out the reasons behind the popularity of a person based on the analysis of events and the evaluation of a Web-based semantic set similarity measure applied to tweets. The methodology uses the semantic similarity measure to group similar tweets in events. Although the tweets cannot contain identical hashtags, they can refer to a unique topic with equivalent or related terminology. A special data structure maintains event information, related keywords and statistics to extract the reasons supporting popularity.FindingsAn implementation of the algorithms has been experimented on a data set of 218,490 tweets from five different countries for popularity detection and reasons extraction. The experimental results are quite encouraging and consistent in determining the reasons behind popularity. The use of Web-based semantic similarity measure is based on statistics extracted from search engines, it allows to dynamically adapt the similarity values to the variation on the correlation of words depending on current social trends.Originality/valueTo the best of the authors’ knowledge, the proposed method for finding the reason of popularity in short messages is original. The semantic set similarity presented in the paper is an original asymmetric variant of a similarity scheme developed in the context of semantic image recognition.

Transmit beamspace-based DOD and DOA estimation method for bistatic MIMO radar

Abstract In this paper, we propose a novel angle estimation method in bistatic multiple-input multiple-output (MIMO) radar, which combines the transmit beamspace (TB) technique with the unitary ESPRIT (U-ESPRIT) model. The TB matrix is designed to focus the transmitted energy within the desired spatial sector, thus achieving the transmit coherent gain. Due to the special data structure of the TB-based bistatic MIMO radar, a corresponding method is proposed to construct the real-valued model. Unfortunately, the existed interpolation error inevitably degrades the direction-of-departure (DOD) estimation performance. To compensate the DOD estimation error, a look-up table is created, which establishes a one-to-one mapping relationship for DOD. The Cramer-Rao bound (CRB) on angle estimation in TB-based bistatic MIMO radar is also derived for performance analysis. Compared with existing methods, the proposed algorithm has better estimation performance due to the improved signal-to-noise ratio (SNR) gain and requires less computational complexity. Numerical simulations are presented to demonstrate the estimation performance of the proposed algorithm.

Denoising influence on discrete frequency classification results for quantum cascade laser based infrared microscopy

Currently, there is great interest in bringing the application of IR spectroscopy into the clinic. This however will require a significant reduction in measurement time as Fourier Transform Infrared (FT-IR) imaging takes hours to days to scan a clinically relevant specimen. A potential remedy for this issue is the use of Quantum Cascade Laser Infrared (QCL IR) microscopy performed in Discrete Frequency (DF) mode for maximum speed gain. This gain could be furthermore improved by applying a proper denoising algorithm that takes into account the specific data structure. We have recently compared spectral and spatial denoising techniques in the context of Fourier Transform IR (FT-IR) imaging and showed that the optimal methods depend heavily on the exact data structure. In general multivariate denoising methods such as Principal Component Analysis (PCA) and Minimum Noise Fraction (MNF) are the most effective for a dataset containing multiple bands. Histologic classification of QCL IR images of pancreatic tissue using Random Forest was therefore performed to investigate which denoising schemes are the most optimal for such experimental data structure. This work is the first to show the effects of denoising on classification accuracy of QCL data and is likely to be transferable to other QCL microscopes and other modalities using DF imaging, e.g. AFM-IR or CARS/SRS imaging.

Path Representation in Circuit Netlists Using Linear-Sized ZDDs with Optimal Variable Ordering

The efficient representation and manipulation of a large number of paths in a Directed Acyclic Graph (DAG) requires the usage of special data structures that may become of exponential size with respect to the size of the graph. Several methodologies targeting Electronic Design Automation problems such as timing analysis, physical design, verification and testing involve path representation and necessary manipulation. Previous works proposed an encoding using Zero-suppressed Binary Decision Diagrams (ZDDs), which has been shown experimentally to cope well when representing structural or logical paths in VLSI circuits. However, it is well known that the ordering of the variables in a ZDD highly affects its size and, therefore, the efficiency of the methodologies utilizing these data structures. In this work, we show that using a reverse topological order for the ZDD variables bounds the number of nodes in the ZDD representing structural paths to the number of edges in the DAG considered, hence, making the ZDD size linear to the DAG’s size. This result, supported here both theoretically and experimentally, is very important as it can render methodologies with questionable scalability applicable to larger industrial designs. We demonstrate the applicability of the proposed variable ordering in one such methodology which utilizes ZDDs to grade the Path Delay Fault coverage of a given test set.

Multigraph modeling and adaptive large neighborhood search for the vehicle routing problem with time windows

In this paper we propose a multigraph model and a heuristic for the Vehicle Routing Problem with Time Windows (VRPTW). In the classical VRPTW, travel information is commonly represented with a customer-based graph, where an arc is an abstraction of the best road-network path between two nodes. We consider the case when parallel arcs are added to this graph to introduce different compromises between travel time and cost. It has been shown in the literature that this multigraph modeling enables substantial gains in the solution quality, while highly complicating the problem. We develop an Adaptive Large Neighbourhood Search (ALNS) heuristic in which a special data structure and dynamic programming algorithms are used to efficiently address the multigraph setting. Computational experiments on several set of instances demonstrate the effectiveness of our solution method and the impact of alternative paths on the solution quality.

Predicting matches in international football tournaments with random forests

Many approaches that analyse and predict results of international matches in football are based on statistical models incorporating several potentially influential covariates with respect to a national team's success, such as the bookmakers’ ratings or the FIFA ranking. Based on all matches from the four previous FIFA World Cups 2002–2014, we compare the most common regression models that are based on the teams’ covariate information with regard to their predictive performances with an alternative modelling class, the so-called random forests. Random forests can be seen as a mixture between machine learning and statistical modelling and are known for their high predictive power. Here, we consider two different types of random forests depending on the choice of response. One type of random forests predicts the precise numbers of goals, while the other type considers the three match outcomes—win, draw and loss—using special algorithms for ordinal responses. To account for the specific data structure of football matches, in particular at FIFA World Cups, the random forest methods are slightly altered compared to their standard versions and adapted to the specific needs of the application to FIFA World Cup data.

Layered infill area generation from triply periodic minimal surfaces for additive manufacturing

Compared with widely applied tessellation mesh or parametric spline surfaces, implicit surfaces are more suitable for representing models with complicated topology and geometry. Triply periodic minimal surface (TPMS) is a kind of charming implicit surface talented at designing porous structures. Layered infill area generation is the crucial task of process planning for additive manufacturing (AM). Generally, the constructed TPMS are open surfaces with zero thickness, which cannot be directly fabricated. In this work, a complete method is proposed to generate layered infill areas from TPMS for AM. Initially, based on the Marching Squares (MS) algorithm, the TPMS can be efficiently sliced into numerous unstructured line segments. Afterwards, an efficient sorting algorithm with a linear time complexity is developed by special data structures to generate ordered slicing contours. Eventually, in virtue of the demonstrated bidirectional-offset-union strategy, close polygons can be generated as layered infill areas for path planning procedure to manufacture TPMS structures with uniform wall thickness. Experimental results verify the effectiveness and merits of the proposed approach, which can also be expanded to fabricate other complicated implicit surfaces via AM technology.

ANALYSIS OF HEAVY RAIN FROM TYPHOON NUMBER 3 (2017) USING X-BAND MULTIPARAMETER RADAR DATA, KYUSHU, JAPAN

Abstract. The heavy rain on 5–6 July of 2017 was generated by the seasonal rain front that lingered from 30 June until 4 July, and by typhoon No.3 in the Kyushu northern part of Japan (Fukuoka and Oita Prefectures) which occurred at 09:00 on July 2. Such heavy rain caused serious disasters (landslides and flooding). During the large-scale downpour, 2617 damaged dwellings and 39 deaths were recorded in Oita and Fukuoka Prefectures. A new radar system, the X-band MP radar system, provides more detailed rainfall observations than C-band radar, but its special data structure precludes an easy data processing by geographical information systems (GISs) and other software. To overcome this difficulty, we developed an original software that is compatible with GIS. The rainfall amount accumulated by a monitoring system is useful in disaster prevention, as dangerous levels of the accumulated rainfall provide a warning signal.

SOGC: Implementing surrogate object garbage collector management for a Mobile Cloud Environment

SummaryThe aim of an ideal distributed Mobile Cloud environment, the surrogate object, is an agent for a particular Mobile Host in the wired or wireless network with specific data structures and methods. The Surrogate object is a software entity that is hosted on mobile support station and acts on behalf of a mobile host during the disconnection operation. The mobile hosts are registered with their own unique identifier in their cloud environment with their mobile support system. If the registered Mobile host is migrated from one cloud to another, the surrogate object handovers the data or resource management to another cloud with a new object with the same entities of old surrogate object, which leads inefficient. The granularity of object migration the old object is useless after the old object switched over into the new object. The collection of unused surrogate object is a big challenge. This problem will be achieved through the garbage collector technique in Mobile Cloud computing. In this research, we propose the Surrogate Object Garbage Collector (SOGC) model with new middleware consists in detecting the unused surrogate object by Mark‐Compact Garbage Collection technique to recycle their object resources. SOGC periodically recycles the idle object and automatically reuses it; whenever it is needed to manage cloud environment, it results in quality‐of‐execution, low latency, and cost optimization in data management.

Matrix-Free Nodal Domain Decomposition With Relaxation For Massively Parallel Finite-Element Computation of EM Apparatus

In this paper, the nodal domain decomposition with relaxation (NDDR) scheme is proposed to solve the nonlinear finite-element (FE) problem in electromagnetic apparatus without assembling the global system of equations. Each sub-domain contains only one node with unknown magnetic vector potential, and the calculation of each sub-domain can be massively parallelized to utilize the prevalent parallel computing architectures. The sub-domain solver has excellent modularity for single instruction multiple data programming with a specific data structure, and the required memory shows a linear increase with the problem size. The NDDR scheme is implemented on both multi-core CPUs and many-core GPUs, and the accuracy and efficiency are discussed for different problem sizes. Result comparison with a commercial FE package shows a speedup of more than 30 times for a magnetostatic case and an average speedup of more than 53 times for a time-domain nonlinear FE case with different time steps while maintaining an error of less than 0.85%.

Loop-invariant Optimization in the Pifagor Language

The paper considers methods of program transformation equivalent to optimizing the cycle invariant, applied to the functional data-flow model implemented in the Pifagor programming language. Optimization of the cycle invariant in imperative programming languages is reduced to a displacement from the cycle of computations that do not depend on variables that are changes in the loop. A feature of the functional data flow parallel programming language Pifagor is the absence of explicitly specified cyclic computations (the loop operator). However, recurring calculations in this language can be specified recursively or by applying specific language constructs (parallel lists). Both mechanisms provide the possibility of parallel execution. In the case of optimizing a recursive function, repeated calculations are carried out into an auxiliary function, the main function performing only the calculation of the invariant. When optimizing the invariant in computations over parallel lists, the calculation of the invariant moves from the function that executes over the list items to the function containing the call. The paper provides a definition of ”invariant” applied to the Pifagor language, algorithms for its optimization, and examples of program source codes, their graph representations (the program dependence graph) before and after optimization. The algorithm shown for computations over parallel lists is applicable only to the Pifagor language, because it rests upon specific data structures and the computational model of this language. However, the algorithm for transforming recursive functions may be applied to other programming languages.

Incrementally updating unary inclusion dependencies in dynamic data

Inclusion dependencies form one of the most fundamental classes of integrity constraints. Their importance in classical data management is reinforced by modern applications like data profiling, data cleaning, entity resolution, and schema matching. Their discovery in an unknown dataset is at the core of any data-analysis effort. Therefore, several research approaches have focused on their efficient discovery in a given, static dataset. However, none of these approaches are appropriate for application on dynamic datasets. In these cases, discovery techniques should be able to efficiently update the inclusion dependencies after an update in the dataset, without reprocessing the entire dataset. We present the first approach for incrementally updating the unary inclusion dependencies. In particular, our approach is based on the concept of attribute clustering, from which the unary inclusion dependencies are efficiently derivable. We incrementally update the clusters after each update of the dataset. An update of the clusters does not need access to the dataset because of special data structures designed to efficiently support the updating process. We performed an exhaustive analysis of our approach by applying it to large datasets with several hundred attributes and more than 116.2 million tuples. The results showed that the incremental discovery significantly reduces the runtime needed by the static discovery. This reduction in the runtime is up to 99.9996% for both the insertion and the deletion.

Efficient Information Flow Maximization in Probabilistic Graphs

Reliable propagation of information through large networks, e.g., communication networks, social networks or sensor networks is very important in many applications concerning marketing, social networks, and wireless sensor networks. However, social ties of friendship may be obsolete, and communication links may fail, inducing the notion of uncertainty in such networks. In this paper, we address the problem of optimizing information propagation in uncertain networks given a constrained budget of edges. We show that this problem requires to solve two NP-hard subproblems: the computation of expected information flow, and the optimal choice of edges. To compute the expected information flow to a source vertex, we propose the F-tree as a specialized data structure, that identifies independent components of the graph for which the information flow can either be computed analytically and efficiently, or for which traditional Monte-Carlo sampling can be applied independently of the remaining network. For the problem of finding the optimal edges, we propose a series of heuristics that exploit properties of this data structure. Our evaluation shows that these heuristics lead to high quality solutions, thus yielding high information flow, while maintaining low running time.

Quantitative Aspects of Linear and Affine Closed Lambda Terms

Affine λ-terms are λ-terms in which each bound variable occurs at most once, and linear λ-terms are λ-terms in which each bound variable occurs once and only once. In this article, we count the number of affine closed λ-terms of size n , linear closed λ-terms of size n , affine closed β-normal forms of size n , and linear closed β-normal forms of size n , for several measures of the size of λ-terms. From these formulas, we show how we can derive programs for generating all the terms of size n for each class. The foundation of all of this is a specific data structure, made of contexts in which one counts all the holes at each level of abstractions by λ’s.

GPU-based Acceleration of Methods based on Clock Matching Metric for Large Scale 3D Shape Retrieval

In this paper, we exploit the potential of the GPU in order to accelerate the process of 3D shape retrieval in large databases. Indeed, the massive parallelism of the GPU offers a huge performance in much high-performance computing (HPC) applications. Our solution consists to accelerate the shape matching process of methods that use a specific similarity metric called Clock Matching (CM). This CM measure is used by view-based methods as an efficient solution to compare two 3D models even if they are not presented in same pose and orientation by taking into account all possible poses in the matching phase. However, the increase in the number of comparisons has a strong influence on the execution time. Our challenge is to exploit the maximum benefit of GPU computing resource by considering the difficulty of implementing the CM metric on GPU. Indeed, the descriptor of a given 3D object is organized using a specific data structure (hash table), where only the information whose values are not equal to zero appears in the feature vector, which makes the parallelization on GPU to be not trivial. Experiment results show a reasonable benefit from the GPU approach.

A VR simulation framework integrated with multisource CAE analysis data for mechanical equipment working process

Nowadays, the virtual reality (VR) technology has been widely used in machinery to verify the working process and performance of equipment prototype in early design stage. To gain insight into the coupling of process verifying the timing and rhythm of equipment working and performance indicating the quality of equipment, a VR framework is proposed in this paper to integrate the modeling and simulation of working process with the visualization of multisource analysis data from various computer-aided engineering (CAE) software. The framework is equipped with an extended discrete event system specification (DEVS) method, named scripted DEVS, which divides the process modeling into two stages, namely, behavior modeling and data customization, and frees the mechanical designer from programming. Based on the method, a uniform procedure of data integration is designed to hide the specific structure and visualization implementation of diverse data from the modeler by a script format called PDTree (Process&Data Tree) and a series of visualization interfaces in model-view-controller (MVC) pattern. Three applications of different working processes are also presented finally to verify the flexibility and compatibility of the framework.

Prime Implicate Generation in Equational Logic

We present an algorithm for the generation of prime implicates in equational logic, that is, of the most general consequences of formulæ containing equations and disequations between first-order terms. This algorithm is defined by a calculus that is proved to be correct and complete. We then focus on the case where the considered clause set is ground, i.e., contains no variables, and devise a specialized tree data structure that is designed to efficiently detect and delete redundant implicates. The corresponding algorithms are presented along with their termination and correctness proofs. Finally, an experimental evaluation of this prime implicate generation method is conducted in the ground case, including a comparison with state-of-the-art propositional and first-order prime implicate generation tools.

How to Obtain Valid Inference under Unit Nonresponse?

Abstract Weighting methods are commonly used in situations of unit nonresponse with linked register data. However, several arguments in terms of valid inference and practical usability can be made against the use of weighting methods in these situations. Imputation methods such as sample and mass imputation may be suitable alternatives, as they lead to valid inference in situations of item nonresponse and have some practical advantages. In a simulation study, sample and mass imputation were compared to traditional weighting when dealing with unit nonresponse in linked register data. Methods were compared on their bias and coverage in different scenarios. Both, sample and mass imputation, had better coverage than traditional weighting in all scenarios. Imputation methods can therefore be recommended over weighting as they also have practical advantages, such as that estimates outside the observed data distribution can be created and that many auxiliary variables can be taken into account. The use of sample or mass imputation depends on the specific data structure.

Causality-based Model Checking

Model checking is usually based on a comprehensive traversal of the state space. Causality-based model checking is a radically different approach that instead analyzes the cause-effect relationships in a program. We give an overview on a new class of model checking algorithms that capture the causal relationships in a special data structure called concurrent traces. Concurrent traces identify key events in an execution history and link them through their cause-effect relationships. The model checker builds a tableau of concurrent traces, where the case splits represent different causal explanations of a hypothetical error. Causality-based model checking has been implemented in the ARCTOR tool, and applied to previously intractable multi-threaded benchmarks.

Digital anthropology as method for lead user identification from unstructured big data

This paper focuses on lead user identification in an open data context through the principles of digital anthropology (DA). The approach is demonstrated using a case study from the entertainment industry: the festival of Tomorrowland. Following the principles of DA, the specific data structure of the case selected is studied. This structure follows the tribal roles of Cova and Cova, which are then compared with the lead user characteristics of Belz and Baumbach. The different characteristics and roles are used afterwards to establish the user types that may be found in the big data set of the case study. Unlike earlier methods, such as netnography, which focuses on a specific user type as the main source of insights, our results show that DA requires a change of focus from specific individuals to groups of individuals with the capacity to produce the insights of a lead user. DA shows how all the roles in the data structure present different possibilities for innovation. The application of DA to big data enables the extension of analysis to whole populations instead of specific samples, bringing about new possibilities to model and extract the insights of consumers publicly exposed in the digital media.