Efficient Access Method Research Articles

Iodine-Catalyzed Cascade Annulation of 4-Hydroxycoumarins with Aurones: Access to Spirocyclic Benzofuran-Furocoumarins.

An attractive approach for the preparation of spirocyclic benzofuran-furocoumarins has been developed through iodine-catalyzed cascade annulation of 4-hydroxycoumarins with aurones. The reaction involves Michael addition, iodination, and intramolecular nucleophilic substitution in a one-step process, and offers an efficient method for easy access to a series of valuable spirocyclic benzofuran-furocoumarins in good yields (up to 99%) with excellent stereoselectivity. Moreover, this unprecedented protocol provides several advantages, including readily available materials, an environmentally benign catalyst, a broad substrate scope, and a simple procedure.

Accelerating Sampling and Aggregation Operations in GNN Frameworks with GPU Initiated Direct Storage Accesses

Graph Neural Networks (GNNs) are emerging as a powerful tool for learning from graph-structured data and performing sophisticated inference tasks in various application domains. Although GNNs have been shown to be effective on modest-sized graphs, training them on large-scale graphs remains a significant challenge due to the lack of efficient storage access and caching methods for graph data. Existing frameworks for training GNNs use CPUs for graph sampling and feature aggregation, while the training and updating of model weights are executed on GPUs. However, our in-depth profiling shows CPUs cannot achieve the graph sampling and feature aggregation throughput required to keep up with GPUs. Furthermore, when the graph and its embeddings do not fit in the CPU memory, the overhead introduced by the operating system, say for handling page-faults, causes gross under-utilization of hardware and prolonged end-to-end execution time. To address these issues, we propose the GPU Initiated Direct Storage Access (GIDS) dataloader, to enable GPU-oriented GNN training for large-scale graphs while efficiently utilizing all hardware resources, such as CPU memory, storage, and GPU memory. The GIDS dataloader first addresses memory capacity constraints by enabling GPU threads to directly fetch feature vectors from storage. Then, we introduce a set of innovative solutions, including the dynamic storage access accumulator, constant CPU buffer, and GPU software cache with window buffering, to balance resource utilization across the entire system for improved end-to-end training throughput. Our evaluation using a single GPU on terabyte-scale GNN datasets shows that the GIDS dataloader accelerates the overall DGL GNN training pipeline by up to 582× when compared to the current, state-of-the-art DGL dataloader.

Optimizing Relay Sensors in Large-Scale Wireless Sensor Networks: A Biologically Inspired Approach

In recent years, tremendous advances in communication technologies coupled with the advent of the Internet of Things (IoT) have led to the emergence of the Big Data phenomenon. Big Data is one of the big IT challenges of the current decade. The amount of data produced is constantly increasing and makes it more and more difficult to process. Managing these masses of data requires the use of new data management systems with efficient access methods. Considered as one of the main sources of Big Data, wireless sensors used in networks offer a credible solution to the problem of Big Data management, especially its collection. Several solutions for Big Data collection based on large-scale wireless sensor networks (LS-WSN) are proposed, taking into account the nature of the applications. The hierarchical architecture is the one used for the deployment of these applications. In such an architecture, relay sensors play an important role in finding the balance of the network and maximizing its lifetime. In most LS-WSN applications, once deployed, the LS-WSN does not provide a mechanism to evaluate and improve the positions of the initially deployed relay sensors. This paper proposes, based on the growth model of physarum polycephalum and its ability to prune unnecessary links and retain only those deemed useful for food routing, a mechanism for evaluating and optimizing relay sensors in LS-WSNs. Simulation results indicate that the proposed approach significantly improves the network lifetime compared to the initial deployment and that can be a useful approach for LS-WSNs dedicated to Big Data collection. The effectiveness of the proposed technique is demonstrated by experimental results in terms of connectivity and network lifetime.

Short-Term Estimation and Prediction of Pedestrian Density in Urban Hot Spots Based on Mobile Phone Data

Short-term estimation and prediction of pedestrian density in urban hot spots (e.g., railway station, shopping mall, etc.) is an important topic for traffic management and control in densely populated areas. In this paper, we propose a short-term pedestrian density estimation and prediction method based on mobile phone data. Firstly, pedestrian density in hot spots is estimated using mobile phone data. To decrease the positioning errors of mobile phone data, a modified particle filter method, which considers the movements of pedestrians, is applied for pre-processing the data. An efficient spatial access method (i.e., Hilbert R-tree) is adopted to construct pedestrians’ position indexes for realizing the short-term estimation. Secondly, based on the estimation results, the spatiotemporal extended Kalman filter (SEKF) is proposed for the short-term prediction of pedestrian density. A massive mobile phone dataset collected in Nanjing, China is used in the case study. The estimated pedestrian density from Monday to Thursday is used for pedestrian density prediction on Friday. The results show that the proposed method can estimate and predict pedestrian density in hot spots, especially in small-scale sites of hot spots efficiently in a short time. Comparing with classical prediction methods, the proposed SEKF method predicts short-term pedestrian density in urban hot spots more accurately.

Data Alignment on Embedded CPUs for Programmable Control Devices

This work aims to investigate the impact of memory access limitations in microcontrollers and microprocessors on the performance of software that deals with binary data. The research area covers control systems that process data from the IEC 61131-3 standard using a software-implemented virtual machine. Three methods of memory access are considered, namely byte access, memory copying, and direct pointer. Tests of these methods are performed on several CPUs with ARM architecture (with variants), MIPS, RISC-V, Quark, and others, often used as hardware platforms for control devices. The tests cover 1-, 2-, 4-, and 8-byte data sizes, which correspond to the integer types of the IEC 61131-3 standard. By analyzing the results covering both unaligned and aligned data, the goal of this paper is to indicate which of the memory access methods is the most efficient for a particular platform. The research is supplemented with an evaluation of power and memory requirements for a group of STM32 microcontrollers. Therefore, the contribution of this paper rests in indicating the most efficient memory access method for each of more than a dozen CPUs intended for control applications, with consideration of power and memory requirements.

PRACTICAL EXAMPLES ON BIM-GIS INTEGRATION BASED ON SEMANTIC WEB TRIPLESTORES

Abstract. The integration of geodata and building models is one of the current challenges in the AECOO (architecture, engineering, construction, owner, operation) domain. Data from Building Information Models (BIM) and Geographical Information Systems (GIS) can’t be simply mapped 1:1 to each other because of their different domains. One possible approach is to convert all data in a domain-independent format and link them together in a semantic database. To demonstrate, how this data integration can be done in a federated database architecture, we utilize concepts of the semantic web, ontologies and the Resource Description Framework (RDF). It turns out, however, that traditional object-relational approaches provide more efficient access methods on geometrical representations than triplestores. Therefore we developed a hybrid approach with files, geodatabases and triplestores. This work-in-progress-paper (extend abstract) demonstrates our intermediate research results by practical examples and identifies opportunities and limitations of the hybrid approach.

A Review of Orbital Angular Momentum Vortex Waves for the Next Generation Wireless Communications

The next-generation wireless technology that can fulfill such a demand, namely the fifth-generation (5G) technology, should provide 1000 times larger capacity. Moreover, sixth-generation (6G) communication, which represents a significant upgrade from the fifth-generation (5G) network and is anticipated to operate from 100 GHz to 3 THz band, will be required in the years after 2030 due to newly developed data-hungry applications and the greatly expanded wireless network. To meet the ever-growing demands of wireless carriers, an efficient wireless access method that can improve wireless area throughput without expanding bandwidth or cell size is required. Radio Frequency (RF) Orbital Angular Momentum vortex waves (which is now on referred to as OAM waves) to address the concerns mentioned above have attracted much attention in recent years. Due to their orthogonality, different OAM waves of different modes can be multiplexed in the same frequency channel, which can greatly increase the channel capacity. Using the orthogonal modes, a new type of multiple access scheme known as Mode Domain Multiple Access (MDMA) can be used by multiple users using the same frequency channel without additional resources such as frequency and time. As a result, the channel capacity for the next generation wireless communication systems can be enhanced as well as the overall spectrum efficiency can be improved. This review paper begins with an overview of the next generation communication such as 5G communication technology and beyond. This paper first briefly discusses the theory of OAM waves and several methods to generate OAM waves. Various different designs have also been analyzed for their ability to generate OAM waves and discussion on several restrictions and solutions to resolve. Open concerns and development trends are discussed for possible future RF OAM antenna upgrades. This study also proposes that for next generation wireless communication employing OAM, the typically used Uniform Circular Array (UCA) could be paired with the Multiple-Input-Multiple-Output (MIMO) system to improve performance in dense or urban areas for multiusers. In addition, the purity of OAM-modes needs to be considered for efficient utilization of the OAM system for future communications at the radio domain.

An efficient routing access method based on multi-agent reinforcement learning in UWSNs

An efficient routing access method based on multi-agent reinforcement learning in UWSNs

High-Speed FPGA Implementation of SIKE Based on an Ultra-Low-Latency Modular Multiplier

The supersingular isogeny key encapsulation (SIKE) protocol, as one of the post-quantum protocol candidates, is widely regarded as the best alternative for curve-based cryptography. However, the long latency, caused by the serial large-degree isogeny computation which is dominated by modular multiplications, has made it less competitive than most popular post-quantum candidates. In this paper, we propose a high-speed and low-latency architecture for our recently presented optimized SIKE algorithm. Firstly, we design a new field arithmetic logic unit (FALU) with many algorithmic transformations and architectural optimizations. Especially, for the FALU, an extremely low-latency modular multiplier is devised based on a modified algorithm by fully parallelizing and highly optimizing the small-size multipliers and the reduction submodules. Secondly, we develop a compact control logic and update the instructions based on the benchmark provided in the newest SIKE library, fitting well with our design. Thirdly, an efficient memory access method is proposed by scheduling the input and output of the arithmetic logic unit (ALU) in two identical RAMs, which can significantly reduce the latency. Finally, we code the proposed architectures using the Verilog language and integrate them into the SIKE library. The implementation results on a Xilinx Virtex-7 FPGA show that for SIKEp751, our design only costs 9.3 ms with a frequency of 155.8 MHz, about 2× faster than the state-of-the-art, and achieves the best area efficiency among existing works. Particularly, the modular multiplier merely needs 16 clock cycles, reducing the delay by nearly one order of magnitude with a small factor of increase in hardware resource.

Efficient access methods for very large distributed graph databases

Subgraph searching is an essential problem in graph databases, but it is also challenging due to the involved subgraph isomorphism NP-Complete sub-problem. Filter-Then-Verify (FTV) methods mitigate performance overheads by using an index to prune out graphs that do not fit the query in a filtering stage, reducing the number of subgraph isomorphism evaluations in a subsequent verification stage. Subgraph searching has to be applied to very large databases (tens of millions of graphs) in real applications such as molecular substructure searching. Previous surveys have identified the FTV solutions GraphGrepSX (GGSX) and CT-Index as the best ones for large databases (thousands of graphs), however they cannot reach reasonable performance on very large ones (tens of millions graphs). This paper proposes a generic approach for the distributed implementation of FTV solutions. Besides, three previous methods that improve the performance of GGSX and CT-Index are adapted to be executed in clusters. The evaluation shows how the achieved solutions provide a great performance improvement (between 70% and 90% of filtering time reduction) in a centralized configuration and how they may be used to achieve efficient subgraph searching over very large databases in cluster configurations.

HyR-tree: a spatial index for hybrid flash/3D XPoint storage

Flash-based SSDs have become well established in the storage market, replacing magnetic disks in both enterprise and consumer computer systems. The performance characteristics of these new devices have prompted a considerable amount of research that aims at developing efficient data access methods. Early works attempted to reduce the expensive random writes, exploiting logging and batch write techniques, while more recent ones addressed query processing, taking advantage of the high internal parallelism of SSDs. 3D XPoint is a new nonvolatile memory technology that has emerged recently, featuring smaller access times and higher durability compared with flash. It is available both as block addressable secondary storage and as byte addressable persistent main memory. However, the high cost of 3D XPoint prevents, for the moment, its adoption in large scales. This renders hybrid storage systems utilizing NAND flash and 3D XPoint a sufficient alternative. In this work, we propose HyR-tree, a hybrid variant of R-tree that persists a part of the tree in the high performing 3D XPoint storage. HyR-tree identifies repeated access pattern to the data and uses these patterns to locate the most important nodes. The importance of a node is determined by the performance gain that derives from its placement within a 3D XPoint-based device. We experimentally evaluated HyR-tree using real devices and four different datasets. The acquired results show that our proposal achieves significant performance gains up to 40% in tree construction and up to 56% in range queries.

Geological tetrahedral model-oriented hybrid spatial indexing structure based on Octree and 3D R*-tree

The lack of efficient access and query method remains a barrier for three-dimensional (3D) geological tetrahedral models (GTMs) to support high-performance 3D spatial analysis and calculation. To organize and manage GTMs efficiently, this paper proposes a GTM-oriented hybrid spatial indexing structure, named 3DOR*-tree, which can fully exploit the advantages of the fast spatial partition of Octree and the efficient spatial query of 3D R*-tree. GTM-oriented data structures are designed on the basis of 3DOR*-tree by setting appropriate thresholds for construction. Geological spatial data query based on 3DOR*-tree can then be implemented. Through test verification and comparative analysis, the accuracy and efficiency of the proposed spatial indexing structure are verified. The node-splitting thresholds of the 3DOR*-tree indexing structure are discussed and analyzed. The 3DOR*-tree can implement efficient construction and query simultaneously. Results corroborate that the node-splitting threshold of Octree is recommend to be set as 500 tetrahedrons, and that the node-splitting thresholds of 3D R*-tree are recommended to be set as 128 and 256 individuals (minimum and maximum tetrahedrons, respectively). This study not only provides technical support for the storage, management, and analysis of GTMs but also provides reference for the theoretical research of a hybrid spatial indexing method.

Security assessment of data management systems for cyber physical system applications

AbstractCyber physical system (CPS) applications are widely used to control critical infrastructure of various application domains, eg, medical health care, energy, and power, to name a few. Such applications usually take input data from sensors, estimate current state of the system, and then based on the estimation, make critical decisions to control the underlying infrastructure automatically. Therefore, security and integrity of the (system state) data are critically important to ensure safe operations of CPS. In this paper, we present a review of security of various data management systems used in CPS. Since CPS are composed of systems of (sub)systems that generate a huge amount of data (ie, periodical sensor input data), therefore, recently, NoSQL and NewSQL data management systems have emerged as popular data management systems to support efficient and scalable analysis of unstructured data. Unfortunately, these systems were not initially build for data security and thus are vulnerable to numerous security attacks. Considering flexible data model and efficient access methods in NoSQL and NewSQL, we discuss the security attacks on such data management systems and their corresponding solutions to mitigate them. In particular, we analyze the system and data security of popular NoSQL and NewSQL systems. To analyze that, we defined feature vectors for system and data security and compared the data systems against them. Finally, we propose security solutions for data management systems by identifying various security vulnerabilities in internal security algorithms of such systems.

An efficient PyAOP-based C4-amination method for direct access of oxidized 5MedC derivatives

In the past decade, synthetic oxidized 5-MedC nucleosides and their derivatives have become essential tools for epigenetic research. The low efficacy of both conventional and newly reported BOP methods on C4-amination of these specific oxidized 5-MedU substrates urged us to systematically investigate how the nature of onium salt-based coupling reagents affects the C4-amination of pyrimidine nucleobases and lead us to the findings that different onium coupling reagents result in the formation of distinctive activation intermediates and PyAOP is much more potent than BOP in both activation and aminolysis steps. Direct amination without the need of ribose protection, ultrafast activation, tolerance to aqueous N-nucleophiles, and excellent yields for diverse oxidized 5MedC derivatives are the advantages of this PyAOP-based C4-amination method.

Interactive and space-efficient multi-dimensional time series subsequence matching

We develop a highly efficient access method, called Delta-Top-Index, to answer top-k subsequence matching queries over a multi-dimensional time series data set. Compared to a naïve implementation, our index has a storage cost that is up to two orders of magnitude smaller, while providing answers within microseconds. Additionally, we apply cache optimization techniques to speed up the construction of the index. Finally, we demonstrate the efficiency and effectiveness of our technique in an experimental evaluation with real-world data.

Parallel Indexing for Past, Current and Future Locations of Moving Objects

Facing huge amounts of location and trajectory data of moving objects, although cloud database systems based on Key-Value mechanism could perform better in scalability than traditional spatio-temporal database systems, it could not provide efficient access method to support querying the locations of moving objects in past, current and future. A parallel index method for past, current and future locations of moving objects named PIPCF is proposed. It splits the space into areas and uses Quad-Tree to manage them first, and then combines temporal property of the data to index the moving objects in each area by R-Tree. Furthermore, a hash table is used to help managing predicted trajectory unit to accelerate the index updating. The experiment shows that PIPCF could perform well in cloud computing environment and improve the querying performance of the locations of moving objects in past, current and future.

Cholic Acid‐Derived Facial Surfactants with Long Side‐Chain Quaternary Ammonium: Synthesis and Antimicrobial Activity Study

AbstractFour cholic acid cationic surfactants containing a flexible long aliphatic chain were synthesized efficiently. A long aliphatic chain was introduced first on the side‐chain carboxyl of cholic acid by one‐pot condensation reaction of cholic acid and C12–C18 amines using DCC (Dicyclohexylcarbodiimide) and HOBt (N‐hydroxybenzotrizole) as condensation agents for the formation of an amide framework. Then reduction of the amide and quaternization gave a cationic group for strengthening hydrophilicity. This strategy offered a very straightforward and efficient method for access to the designed surfactants in good overall yields. Preliminary results show that an increase both in the length of the hydrophobic tail and in the number of charged groups lowered the CMC of cholic acid‐derived cationic surfactants. Cholic acid‐based cationic surfactants containing a flexible longer aliphatic chain and a quaternary ammonium had the highest antimicrobial activity.

WLAN Fairness with <italic>Idle Sense</italic>

Idle Sense (IS) is a simple yet efficient access method to provide optimized throughput and fairness in wireless local area networks (WLAN). Each station dynamically controls its contention window (CW) size by monitoring the mean number of idle slots between transmission attempts. In general this quantity is subject to estimation error that is related to the number of transmissions, $M$ , over which it is measured. Previous works in IS assumed all stations use similar CW sizes to contend the channel; an assumption that is not always valid in reality. Therefore, this paper studies the behaviour of the scheme when stations have different CW sizes. An analysis shows that bias caused by the additive increase multiplicative decrease (AIMD) algorithm used to control the CW sizes, combined with varying lengths of $M$ can cause a fairness problem. Two classes of stations develop, with the first class gaining most of the channel bandwidth while the second class stays idle.

Decomposition tree: a spatio-temporal indexing method for movement big data

Movement is a complex process that evolves through both space and time. Movement data generated by moving objects is a kind of big data, which has been a focus of research in science, technology, economics, and social studies. Movement database is also at the forefront of geographic information science research. Developing efficient access methods for movement data stored in movement databases is of critical importance. Tree-like indexing structures such as the R-tree, Quadtree, Octree are not suitable for indexing multi-dimensional movement data because they all have high space cost of their inner nodes. In addition, it is difficult to use them for parallel access to multi-dimensional movement data because they thereof, are in hierarchical structures, which have severe overlapping problems in high dimensional space. In this paper, we propose a novel access method, the Decomposition Tree (D-tree), for indexing multi-dimensional movement data. The D-tree is a virtual tree without inner nodes, instead, through an encoding method based on integer bit-shifting operation, and can efficiently answer a wide range of queries. Experimental results show that the space cost and query performance of D-tree are superior to its best known competitors.

Aiding Effective Encrypted Document Manipulation Incorporated with Document Categorization Technique in Cloud

Cloud computing involves storage as well as usage of computer resources and services online. It has emerged as a promising area for data outsourcing. Since the users data are stored in servers controlled by cloud service provider there are always concerns about the security of the stored data. The data can be encrypted to ensure its security but it will increase the complexity of the data access process. The presence of large amount of data in the cloud necessitates an efficient method for data access. An efficient keyword based search on the encrypted cloud data is proposed here which provides marked improvement over the traditional boolean search approach used in cloud environment. It uses the latest informational retrieval methods for the processing of the documents from the cloud. The homomorphic encryption technique is used for the building of a secure index which has the key role in the search process. The vector space model is employed for the representation of the text document as vector. Document categorization technique is incorporated for the easy maintenance and retrieval of the data from the cloud. It handles multiple data owners and users with a facility to store and retrieve multiple data files. The approach also makes sure that the processing on the user side is minimized while updating the existing document set.