Proposed Data Structure Research Articles

Algorithms and data structures for Cs-smooth RMB-splines of degree 2s + 1

The simple mesh refinement algorithm of Groiss et al. (2023) generates T-meshes admitting Reachable Minimally supported (RM) B-splines that possess the property of local linear independence and form a non-negative partition of unity. The construction was first presented for the bilinear case and has later been extended to Cs-smooth splines of degree p=2s+1. The present paper is devoted to algorithms and data structures for RMB-splines. We prove that the memory consumption of the data structures for representing a T-mesh and the associated RMB-splines is linear with respect to the mesh size, and we describe the details of the underlying refinement algorithm. Moreover, we introduce a novel evaluation algorithm for RMB-spline surfaces, which is based solely on repeated convex combinations of the control points, thereby generalizing de Boor's algorithm for tensor-product splines. Numerical experiments are included to demonstrate the advantageous behavior of the proposed data structures and algorithms with respect to their efficiency. We observe that the total computational time (which includes also error estimation and spline coefficient computation) scales roughly linearly with the number of degrees of freedom for the meshes considered.

Promoting open science in test-driven software experiments

A core principle of open science is the clear, concise and accessible publication of empirical data, including “raw” observational data as well as processed results. However, in empirical software engineering there are no established standards (de jure or de facto) for representing and “opening” observations collected in test-driven software experiments — that is, experiments involving the execution of software subjects in controlled scenarios. Execution data is therefore usually represented in ad hoc ways, often making it abstruse and difficult to access without significant manual effort. In this paper we present new data structures designed to address this problem by clearly defining, correlating and representing the stimuli and responses used to execute software subjects in test-driven experiments. To demonstrate their utility, we show how they can be used to promote the repetition, replication and reproduction of experimental evaluations of AI-based code completion tools. We also show how the proposed data structures facilitate the incremental expansion of execution data sets, and thus promote their repurposing for new experiments addressing new research questions.

Designing Dynamic Informed Consent For Public Health Research

Abstract Background Current dynamic consent software available is not all tailored for public health research and lacks the appropriate data governance framework and mechanisms to be compliant with public health research requirements. This paper presents the UML modelling of the Dynamic Consent for Public Health (DC4PH), a dynamic consent platform being designed for the Canadian public health domain Methods The proposed platform incorporates data governance and public health stakeholders’ requirements into its design and is based on state-of-the-art standards and solutions. Diagrams created in UML reflect the mapped requirements and demonstrate the capabilities of our dynamic consent proposed data structure. We also incorporated private ledger entities in our modelling to provide an immutable log of dynamic consent for stakeholders and improve transparency and trust between them. Results A series of UML assets were created based on previous mapping of the public health research domain, public health principles, and data governance requirements. Use case diagrams for each type of user of the platform, state diagrams of key business objects vital for the platform's enhancement of traditional consent, and state diagrams of the actor's possible states. Flowchart of the core platform's functionalities, such as the dynamic consent approval process. And a class diagram of the proposed platform that incorporates all the previously mentioned domain mapping and binds it with optional private ledger properties. Conclusions This work offers a comprehensive and extensible UML design that is being used to develop the DC4PH platform. The paper concludes that the design has the potential to improve the efficiency and efficacy of informed consent collection in public health research projects of small or large scales while also being capable of complying with data governance requirements, healthcare standards, and ledger technologies. Key messages • Dynamic informed consent still lacks appropriate scrutiny over the underlining data structures utilized to store and manage dynamic consent. • Our work proposes a design in Unified Modelling Language (UML), of dynamic informed consent based on public health's domain mapping, data governance and public health research requirements.

Dual Cuckoo Filter with a Low False Positive Rate for Deep Packet Inspection

Network traffic control and classification have become increasingly dependent on deep packet inspection (DPI) approaches, which are the most precise techniques for intrusion detection and prevention. However, the increasing traffic volumes and link speed exert considerable pressure on DPI techniques to process packets with high performance in restricted available memory. To overcome this problem, we proposed dual cuckoo filter (DCF) as a data structure based on cuckoo filter (CF). The CF can be extended to the parallel mode called parallel Cuckoo Filter (PCF). The proposed data structure employs an extra hash function to obtain two potential indices of entries. The DCF magnifies the superiority of the CF with no additional memory. Moreover, it can be extended to the parallel mode, resulting in a data structure referred to as parallel Dual Cuckoo filter (PDCF). The implementation results show that using the DCF and PDCF as identification tools in a DPI system results in time improvements of up to 2% and 30% over the CF and PCF, respectively.

ConPoolUBF: Connection pooling and updatable Bloom filter based SYN flood defense in programmable data planes

SYN flood attack is one of the common ways in which attackers take the advantage of TCP’s three-way handshake connection establishment to overwhelm target systems. With the emergence of programmable networks, many promising security functions have been implemented at the network layer. SYN authentication and cookie-based SYN proxy are two significant approaches recommended on programmable switches against SYN flood attacks. However, while the implementation of a cookie-based SYN proxy causes additional delays and packet drops, the SYN authentication approach increases the number of packets required to establish a TCP connection. In this study, two novel functions are implemented on programmable switches using the P4 language, and a new security solution against SYN flood attacks is proposed by combining both functions. The first function is the high-accuracy updatable Bloom filters implemented to track the state of network flows for TCP connection establishment. As the proposed data structure uses a salted input, it is more resistant to target-set coverage attacks. The second is the connection pooling function on programmable switches for requests to the backend servers. In this regard, the TCP three-way handshake is offloaded from the target systems to network switches. Upon the verification of a connection request, dynamic resource allocation is carried out from the connection pool on the P4 switch, enabling the client to connect to the server seamlessly and transparently without the need for additional packets. We implement these functions and demonstrate their feasibility as an effective defense against SYN flood.

In silico antibody-peptide epitope prediction for personalized cancer therapy

The human leukocyte antigen (HLA) system is a complex of genes on chromosome 6 in humans that encodes cell surface proteins responsible for regulating the immune system. Viral peptides presented to cancer cell surfaces by the HLA trigger the immune system to kill the cells, creating antibody–peptide epitopes (APE). This study proposes an in silico approach to identify patient-specific APEs by applying complex networks diagnostics on a novel multiplex data structure as an input for a deep learning model. The proposed analytical model identifies patient- and tumor-specific APEs with as few as 20 labeled data points. In addition, the proposed data structure employs complex network theory and other statistical approaches that can better explain and reduce the black box effect of deep learning. The proposed approach achieves an F1-score of 80% and 93% on patients one and two, respectively, and above 90% on tumor-specific tasks. In addition, it minimizes the required training time and the number of parameters.

Future exposure modelling for risk-informed decision making in urban planning

Population increases and related urban expansion are projected to occur in various parts of the world over the coming decades. These future changes to the urban fabric could fundamentally alter the exposure to natural hazards and the associated vulnerability of people and the built environment with which they interact. Thus, modelling, quantifying, and reducing future urban disaster risk require forward-looking insights that capture the dynamic form of cities. This paper specifically focuses on the exposure component of dynamic natural-hazard disaster risk, by considering urban planning as the centre of future exposure characterisation in a given region. We use the information provided by urban plans and propose an integrated data structure for capturing future exposure to hazards. The proposed data structure provides the necessary detailing for both future physical and socio-demographic exposure in disaster risk modelling. More specifically, it enables users to develop a comprehensive multi-level, multi-scale exposure dataset, characterising attributes of land use, buildings, households and individuals. We showcase the proposed data schema using the virtual urban testbed Tomorrowville. In this case study, we also demonstrate how simplified algorithmic procedures and disaggregation methods can be used to populate the required data. This implementation demonstrates how the proposed exposure data structure can effectively support the development of forward-looking urban visioning scenarios to support decision-making for risk-sensitive and pro-poor urban planning and design in tomorrow’s cities.

IDHUP: Incremental Discovery of High Utility Pattern

<p>As a sub-problem of pattern discovery, utility-oriented pattern mining has recently emerged as a focus of researchers’ attention and offers broad application prospects. Considering the dynamic characteristics of the input databases, incremental utility mining methods have been proposed, aiming to discover implicit information/ patterns whose importance/utility is not less than a user-specified threshold from incremental databases. However, due to the explosive growth of the search space, most existing methods perform unsatisfactorily under the low utility threshold, so there is still room for improvement in terms of running efficiency and pruning capacity. Motivated by this, we provide an effective and efficient method called IDHUP by designing an indexed partitioned utility list structure and employing four pruning strategies. With the proposed data structure, IDHUP can not only dynamically update the utility values of patterns but also avoid visiting non-occurred patterns. Moreover, to further exclude ineligible patterns and avoid unnecessary exploration, we put forward the remaining utility reducing strategy and three other revised pruning strategies. Experiments on various datasets demonstrated that the designed IDHUP algorithm has the best performance in terms of running time compared to state-of-the-art algorithms.</p> <p> </p>

Fair-News: Digital Journalism Model to Prevent Information Pollution and Manipulation

As digital data circulation increases, information pollution and manipulation in journalism have become more prevalent. In this study, a new digital journalism model is designed to contribute to the solution of the main current problems, such as information pollution, manipulation, and accountability in digital journalism. The model uses blockchain technology due to its transparency, immutability, and traceability. However, it is tough to provide the mechanisms necessary for journalism, such as updating one piece of information, instantly updating all other information affected by the updated information, establishing logical relationships between news, making quick comparisons, sorting and indexing news, and keeping the changing information about the news in the system, with the blockchain data structure. For this reason, in our study, we have developed a new data structure that provides both the immutability, transparency and traceability properties of the blockchain and can support the communication mechanisms necessary for journalism. The functionality of our proposed data structure is demonstrated in terms of communication mechanisms such as mutability, context, consistency, and reliability through example scenarios. Additionally, our data structure is compared with the data structure of blockchain technology in terms of time, space, and maintenance costs. Accordingly, while the model size increases linearly in blockchain, the model’s size remains approximately constant since the structure we developed is data-independent. In this way, maintenance costs are reduced. Since our model also has an indexing mechanism, it reduces the linear time search complexity to logarithmic time. As a result, the data structure we developed is found to have higher performance than blockchain in the journalism concept. In future studies, it is planned to test all aspects of the model with a pilot application, eliminate its shortcomings, and develop a holistic approach to the root causes of the problems in the journalism focus.

A generic optimization framework for scheduling problems under machine deterioration and maintenance activities

This paper introduces a novel generic optimization framework for scheduling problems with machine deterioration and maintenance activities. The framework employs computationally efficient and robust data structures for schedules along with templates for implementing optimization algorithms making it applicable within Industry 4.0. The concept includes, but not limited to, the optimization objectives such as the maximum completion time, the maximum lateness (or tardiness), the total (weighted) number of late jobs, the total (weighted) completion times, the total (weighted) tardiness, the just-in-time. A set of example algorithms based on iterative local search, i.e. Nawaz–Enscore–Ham’s method (NEH), simulated annealing, genetic algorithm, scatter search algorithm, artificial bee colony were embedded within the framework to demonstrate its robustness. The theoretical and experimental analysis presented in the paper proved the high efficiency of the proposed approach; it traverses areas containing optimal solutions and at the same time it is characterized by a low computational complexity of related procedures to calculate criterion values or to generate new solutions. Furthermore, if the optimization methods are enhanced by the proposed data structures and procedures, their standard computational complexity is not increased even for complex scheduling problems including maintenance activities. The proposed framework modular architecture enables further integration with new algorithms aiming at improvement of neighbourhood search techniques. Its robustness, efficiency, and modularity can bring advantages in various decision support systems.

DEVELOPMENT OF PLM SYSTEM FOR PRODUCTION PLANNING AND PRODUCTION CONTROL IN SHIPBUILDING

Data integration has become an important issue to develop complex products in shipbuilding. This paper introduces a development of a prototype system for PLM systems in shipbuilding. The PLM system is developed for the purpose of precise production planning and the production management in the shop floor. Especially, the PLM system is considered as a use case to provide input data which is necessary for production simulators that reproduce precise work procedures at the shop floor. The data structure in the PLM system is reviewed to provide the necessary data to the production simulator in the beginning of this paper. The data is represented as BOM (Bill of Materials) and BOP (Bill of Process), which are associated with product information, process information, and facility information, and is implemented in the PLM system. The data flow from the PLM system to the production simulator is introduced, then a series of demonstrations from the PLM system to the execution of the production simulator are presented, followed by a discussion of the effects and the future issues of the proposed data structure and the PLM system.

Configurable Open-source Data Structure for Distributed Conforming Unstructured Homogeneous Meshes with GPU Support

A general multi-purpose data structure for an efficient representation of conforming unstructured homogeneous meshes for scientific computations on CPU and GPU-based systems is presented. The data structure is provided as open-source software as part of the TNL library (https://tnl-project.org/). The abstract representation supports almost any cell shape and common 2D quadrilateral, 3D hexahedron and arbitrarily dimensional simplex shapes are currently built into the library. The implementation is highly configurable via templates of the C++ language, which allows avoiding the storage of unnecessary dynamic data. The internal memory layout is based on state-of-the-art sparse matrix storage formats, which are optimized for different hardware architectures in order to provide high-performance computations. The proposed data structure is also suitable for meshes decomposed into several subdomains and distributed computing using the Message Passing Interface (MPI). The efficiency of the implemented data structure on CPU and GPU hardware architectures is demonstrated on several benchmark problems and a comparison with another library. Its applicability to advanced numerical methods is demonstrated with an example problem of two-phase flow in porous media using a numerical scheme based on the mixed-hybrid finite element method (MHFEM). We show GPU speed-ups that rise above 20 in 2D and 50 in 3D when compared to sequential CPU computations, and above 2 in 2D and 9 in 3D when compared to 12-threaded CPU computations.

A Framework and Tool for Knowledge-Based Seismic Risk Assessment of School Buildings: SLaMA-School

When dealing with seismic risk assessment at a large scale, the collection of relevant building data is still deemed a challenging task, often leading to limited building knowledge and, consequently, high uncertainties. Therefore, innovative yet standardized frameworks and adaptive tools are needed to support the seismic risk assessment of buildings. Towards this goal, this paper proposes a simplified multi-knowledge seismic assessment methodology involving the analytical-mechanical SLaMA (Simple Lateral Mechanism Analysis) method. An ad-hoc data collection form is first developed to identify the building vulnerabilities by merging and building on existing institutional forms at the international level and integrating new input data. The data are then used to implement the SLaMA-based methodology, at different building knowledge levels, to assess the seismic safety and the economic losses of buildings. The proposed data structure and approach is planned to be included in the “Seismic-Response” module for PELL (Public Energy Living Lab)-School platform, aiming to become a standardized and interoperable database for relevant data of Italian schools and a dashboard for allowing stakeholders to continuously monitor their energetic and static/seismic conditions. The paper discusses the potential and effectiveness of the proposed procedure for large-scale applications and its integration into platforms assessing the energy efficiency of buildings.

An incremental algorithm for simultaneous construction of 2D Voronoi diagram and Delaunay triangulation based on a face-based data structure

In this paper, a new incremental algorithm with an overall complexity of O(n logn) for constructing both the 2D Voronoi diagram (VD) and Delaunay triangulation (DT) is proposed. Moreover, a new face-based data structure is proposed which is more cost-effective in terms of memory than the previous edge-based data structures. The proposed data structure maintains the VD and DT in separate matrices, while establishing a connection between these two, allowing extracting neighborhoods and geometric information without the need for further processing in many applications. Moreover, the proposed data structure can be easily converted to the other edge-based data structures such as the Doubly Connected Edge List. The proposed algorithm for finding the nearest neighbor to the added point, in this incremental algorithm, has O(logn) complexity. Evaluations performed by points with different numbers and distributions confirm the high robustness of the proposed algorithm against different point distributions and its O(n logn) overall complexity.

Impact of Input Data Structure on Convolutional Neural Network Energy Prediction Model

Energy demand continues to increase with no prospect of slowing down in the future. This increase is caused by several sociological and economical factors such as population growth, urbanization and technological developments. In view of this growth, it becomes crucial to predict energy consumption for a more accurate management and optimization. Nevertheless, consumption estimation is a complex task due to consumer behaviour fluctuation and weather alterations. Several efforts were proposed in the literature. Almost, all of them focused on improving the prediction model to increase the accuracy of the results. They use the LSTM (Long-Short Term Memory) model to reflect the temporal dependencies between historical data despite its spatial and temporal complexities. The main contribution in this paper is a novel and simple Convolutional Neural Network energy prediction model based on input data structure enhancement. The main idea is to adjust the structure of the input data instead of using a more complicated deep learning model for better performance. The proposed model was implemented, tested using real data and compared to existing ones. The obtained results showed that the proposed data structure has a great influence on the model performance measurement.

Multi-core parallel algorithms for hiding high-utility sequential patterns

High-utility sequential pattern mining (HUSPM) can be applied in many applications such as retail, market basket analysis, click-stream analysis, healthcare data analysis, and bioinformatics. HUSPM algorithms discover useful information from data. However, looking at the dark side, the sensitive patterns can also be disclosed by the competitors, who use a HUSPM algorithm on the leaked data. Therefore, high-utility sequential pattern hiding (HUSPH) is used to protect the privacy information from HUSPM algorithms. This paper proposes three algorithms named High Utility Sequential Pattern Hiding Using Pure Array Structure (USHPA), High Utility Sequential Pattern Hiding Using Parallel Strategy (USHP), and High Utility Sequential Pattern Hiding Using Random Distribution Strategy (USHR) for hiding high-utility sequential patterns on quantitative sequence datasets. These algorithms use a proposed data structure named Pattern Utility Set for Hiding (PUSH) to speed up the hiding process. We also introduce a metric called Privacy Factor to evaluate the quality of hiding results. The comparative experiments were conducted on real datasets to evaluate the performance of the proposed algorithms in terms of runtime, memory consumption, scalability, missing cost, and privacy factor. Results show that the proposed algorithms can efficiently sanitize the input datasets, and they outperform the compared algorithms for all metrics.

An efficient edge based data structure for the compressible Reynolds‐averaged Navier–Stokes equations on hybrid unstructured meshes

AbstractAn efficient edge based data structure has been developed in order to implement an unstructured vertex based finite volume algorithm for the Reynolds‐averaged Navier–Stokes equations on hybrid meshes. In the present approach, the data structure is tailored to meet the requirements of the vertex based algorithm by considering data access patterns and cache efficiency. The required data are packed and allocated in a way that they are close to each other in the physical memory. Therefore, the proposed data structure increases cache performance and improves computation time. As a result, the explicit flow solver indicates a significant speed up compared to other open‐source solvers in terms of CPU time. A fully implicit version has also been implemented based on the PETSc library in order to improve the robustness of the algorithm. The resulting algebraic equations due to the compressible Navier–Stokes and the one equation Spalart–Allmaras turbulence equations are solved in a monolithic manner using the restricted additive Schwarz preconditioner combined with the FGMRES Krylov subspace algorithm. In order to further improve the computational accuracy, the multiscale metric based anisotropic mesh refinement library PyAMG is used for mesh adaptation. The numerical algorithm is validated for the classical benchmark problems such as the transonic turbulent flow around a supercritical RAE2822 airfoil and DLR‐F6 wing‐body‐nacelle‐pylon configuration. The efficiency of the data structure is demonstrated by achieving up to an order of magnitude speed up in CPU times.

Novel Static Multi-Layer Forest Approach and Its Applications

The existing multi-layer tree is of dynamic linked list type which has many limitations and is complicated due to the pointer-node structure. Static array representation gives more flexibility in programming of algorithms and operations like insertion, deletion, and search. It also reduces the storage space. This paper presents a new method for representing multi-layer forest data structure in array format. It also explains various tree operations, unique data compression algorithm and migration algorithm between traditional approach and the proposed data structure. Most of the fundamental algorithms like those from artificial intelligence that employ decision trees are based on trees/forest data structure. The current paper brings a completely new idea in the representation of these data structures without employing recursion and targeting memory optimizations with reduced code complexities. The applications of forest data structures are many and span over various interdisciplinary areas of Engineering, Medicine, Aviation, Locomotive, Marine, etc. The proposed novel approach not just introduces a new method to look at the tree data structure but also provides the flexibility to adapt to the existing methods as per the user needs. A few such applications in Simulink debugging and the Forest visualisation have been discussed in detail in this paper.

C-trie++: A dynamic trie tailored for fast prefix searches

Given a dynamic set K of k strings of total length n whose characters are drawn from an alphabet of size σ, a keyword dictionary is a data structure built on K that provides lookup, prefix search, and update operations on K. Under the assumption that α=w/lg⁡σ characters fit into a single machine word of w bits, we propose a keyword dictionary that represents K in either nlg⁡σ+Θ(klg⁡n) or |T|lg⁡σ+Θ(kw) bits of space, where |T| is the number of nodes of a trie representing K. It supports all operations in O(m/α+lg⁡α) expected time on an input string of length m in the word RAM model. An evaluation of our implementation highlights the practical usefulness of the proposed data structure, especially for prefix searches — one of the most essential keyword dictionary operations.

Succinct Encoding of Binary Strings Representing Triangulations

We consider the problem of designing a succinct data structure for representing the connectivity of planar triangulations. The main result is a new succinct encoding achieving the information-theory optimal bound of 3.24 bits per vertex, while allowing efficient navigation. Our representation is based on the bijection of Poulalhon and Schaeffer (Algorithmica, 46(3):505–527, 2006) that defines a mapping between planar triangulations and a special class of spanning trees, called PS-trees. The proposed solution differs from previous approaches in that operations in planar triangulations are reduced to operations in particular parentheses sequences encoding PS-trees. Existing methods to handle balanced parentheses sequences have to be combined and extended to operate on such specific sequences, essentially for retrieving matching elements. The new encoding supports extracting the d neighbors of a query vertex in O(d) time and testing adjacency between two vertices in O(1) time. Additionally, we provide an implementation of our proposed data structure. In the experimental evaluation, our representation reaches up to 7.35 bits per vertex, improving the space usage of state-of-the-art implementations for planar embeddings.