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With the rapid growth of multimedia data (e.g., text, image, video, audio and 3D model, etc) in the web, there are a large number of media objects with different modalities in the multimedia documents such as webpages, which exhibit latent semantic correlation. As a new type of multimedia retrieval method, cross-media retrieval is becoming increasingly attractive, through which users can get the results with various media types with the same semantic information by submitting a retrieval of any media type. The explosive increasing of the number of media objects, however, makes it difficult for the traditional local standalone mode to process efficiently. So the powerful parallel processing capability of cloud computing is accommodated to facilitate the efficient large-scale cross-media retrieval. In this paper, based on a Multi-Layer-Cross-Reference-Graph(MLCRG) model, we propose an efficient parallel cross-media retrieval (PCMR) method in which two enabling techniques (i.e., 1) the adaptive cross-media data allocation algorithm and 2) the PCIndex scheme) are accommodated to effectively speedup the retrieval performance. To the best of our knowledge, there is little research on the parallel retrieval processing of the large-scale cross-media databases in the mobile cloud network. Extensive experiments are conducted to testify that our proposed PCIndex method outperform the three competitors (e.g., the PFAR (Mao et al, 22), the MBSR (Retrieval 4(2):153-164, 42) and the SPECH (Knowl Based Syst 251(5):1-13, 40)) in terms of the effectiveness and efficiency, respectively. [ABSTRACT FROM AUTHOR]

Interlaced Magnetic Recording (IMR) is an emerging recording technology for hard-disk drives (HDDs) that provides larger storage capacity at a lower cost. By partially overlapping (interlacing) each bottom track with two adjacent top tracks, IMR-based HDDs successfully increase the data density while incurring some hardware write constraints. To update each bottom track, the data on two adjacent top tracks must be read and rewritten to avoid losing their valid data, resulting in additional overhead for performing read-modify-write (RMW) operations. Therefore, researchers have proposed various data management schemes to mitigate such overhead in recent years, aiming at improving the write performance. However, these designs have not taken into account the data characteristics of the file system, which is a crucial layer of operating systems for storing/ retrieving data into/from HDDs. Consequently, the write performance improvement is limited due to the unawareness of spatial locality and hotness of data. This paper proposes a file-system-aware data management scheme called FSIMR to improve system write performance. Noticing that data of the same directory may have higher spatial locality and are mostly updated at the same time, FSIMR logically partitions the IMR-based HDD into fixed-sized zones; data belonging to the same directory will be arranged to one zone to reduce the time of seeking to-be-updated data (seek time). Furthermore, cold data within a zone are arranged to bottom tracks and updated in an out-of-place manner to eliminate RMW operations. Our experimental results show that the proposed FSIMR could reduce the seek time by up to 14% without introducing additional RMW operations, compared to existing designs. [ABSTRACT FROM AUTHOR]

Summary: Cloud providers must find out how to properly arrange data in a limited count of servers while ensuring latency assurances to reduce total storage expenses. Timeout is also important to consider because it has a substantial impact on response latency. The core aim of this task is to implement a new cloud object storage system strategy that handles challenges like "latency‐sensitive data allocation, latency‐sensitive data re‐allocation, and latency‐sensitive workload consolidation." The main contribution here is that distributing the latency of the cloud object storage system allows for better data allocation, data reallocation, and workload consolidation. The primary aim is to use the fewest number of servers feasible to fulfill all requests while maintaining their latency requirements, lowering the overall data transmission cost. As a consequence, Whale Butterfly Optimization Method (WBOA) is a novel hybrid meta‐heuristic algorithm that solves NP‐hard problems by combining baseline advanced algorithms. The simulation outcomes reveal that the offered paradigm consistently provides the greatest outcomes regarding throughput utilization, lower latency, higher storage, and number of used nodes when compared to competing techniques. [ABSTRACT FROM AUTHOR]

Continuous attempts have been made to improve the flexibility and effectiveness of distributed computing systems. Extensive effort in the fields of connectivity technologies, network programs, high processing components, and storage helps to improvise results. However, concerns such as slowness in response, long execution time, and long completion time have been identified as stumbling blocks that hinder performance and require additional attention. These defects increased the total system cost and made the data allocation procedure for a geographically dispersed setup difficult. The load-based architectural model has been strengthened to improve data allocation performance. To do this, an abstract job model is employed, and a data query file containing input data is processed on a directed acyclic graph. The jobs are executed on the processing engine with the lowest execution cost, and the system's total cost is calculated. The total cost is computed by summing the costs of communication, computation, and network. The total cost of the system will be reduced using a Swarm intelligence algorithm. In heterogeneous distributed computing systems, the suggested approach attempts to reduce the system's total cost and improve data distribution. According to simulation results, the technique efficiently lowers total system cost and optimizes partitioned data allocation. [ABSTRACT FROM AUTHOR]

This paper is devoted to the reduction of data transfer between the main memory and direct mapped cache for blocked shortest paths algorithms (BSPA), which represent data by a D[M×M] matrix of blocks. For large graphs, the cache size S = δ×M2, δ < 1 is smaller than the matrix size. The cache assigns a group of main memory blocks to a single cache block. BSPA performs multiple recalculations of a block over one or two other blocks and may access up to three blocks simultaneously. If the blocks are assigned to the same cache block, conflicts occur among the blocks, which imply active transfer of data between memory levels. The distribution of blocks on groups and the block conflict count strongly depends on the allocation and ordering of the matrix blocks in main memory. To solve the problem of optimal block allocation, the paper introduces a block conflict weighted graph and recognizes two cases of block mapping: non-conflict and minimum-conflict. In first case, it formulates an equitable color-class-size constrained coloring problem on the conflict graph and solves it by developing deterministic and random algorithms. In second case, the paper formulates a problem of weighted defective color-count constrained coloring of the conflict graph and solves it by developing a random algorithm. Experimental results show that the equitable random algorithm provides an upper bound of the cache size that is very close to the lower bound estimated over the size of a complete subgraph, and show that a non-conflict matrix allocation is possible at δ = 0.5 for M = 4 and at δ = 0.1 for M = 20. For a low cache size, the weighted defective algorithm gives the number of remaining conflicts that is up to 8.8 times less than the original BSPA gives. The proposed model and algorithms are applicable to set-associative cache as well.

In the multi-access edge computing (MEC) environment, app vendors’ data can be cached on edge servers to ensure low-latency data retrieval. Massive users can simultaneously access edge servers with high data rates through flexible allocations of transmit power. The ability to manage networking resources offers unique opportunities to app vendors but also raises unprecedented challenges. To ensure fast data retrieval for users in the MEC environment, edge data caching must take into account the allocations of data, users, and transmit power jointly. We make the first attempt to study the Data, User, and Power Allocation (DUPA $^3$ 3 ) problem, aiming to serve the most users and maximize their overall data rate. First, we formulate the DUPA $^3$ 3 problem and prove its $\mathcal {NP}$ NP -completeness. Then, we model the DUPA $^3$ 3 problem as a potential DUPA $^3$ 3 game admitting at least one Nash equilibrium and propose a two-phase game-theoretic decentralized algorithm named DUPA $^3$ 3 Game to achieve the Nash equilibrium as the solution to the DUPA $^3$ 3 problem. To evaluate DUPA $^3$ 3 Game, we analyze its theoretical performance and conduct extensive experiments to demonstrate its effectiveness and efficiency. [ABSTRACT FROM AUTHOR]

Because physics‐based models of dynamical systems are constrained to obey conservation laws, they must typically be fed long sequences of temporally consecutive (TC) data during model calibration and evaluation. When memory time scales are long (as in many physical systems), this requirement makes it difficult to ensure distributional similarity when partitioning the data into independent, TC, calibration and evaluation subsets. The consequence can be poor and/or uncertain model performance when applied to new situations. To address this issue, we propose a novel strategy for achieving robust and transferable model performance. Instead of partitioning the data into TC calibration and evaluation periods, the model is run in continuous simulation mode for the entire period, and specific time steps are assigned (via a deterministic data‐allocation approach) for use in computing the calibration and evaluation metrics. Generative adversarial testing shows that this approach results in consistent calibration and evaluation data subset distributions. When tested using three conceptual rainfall‐runoff models applied to 163 catchments representing a wide range of hydro‐climatic conditions, the proposed "distributionally consistent (DC)" strategy consistently resulted in better overall performance than achieved using the traditional "TC" strategy. Testing on independent data periods confirmed superior robustness and transferability of the DC‐calibrated models, particularly under conditions of larger runoff skewness. Because the approach is generally applicable to physics‐based models of dynamical systems, it has the potential to significantly improve the confidence associated with prediction and uncertainty estimates generated using such models. Plain Language Summary: When developing models (whether physical or conceptual) it is common practice to partition the available data into separate calibration and evaluation subsets. Further, these subsets need to consist of temporally consecutive data due to the long residence times (memory) of the system state. Such an approach can result in low robustness and poor generalization ability, due to significant variation in the hydro‐climatic conditions represented by the two subsets. Here, we show that by discarding the idea of partitioning historical data into time‐consecutive subsets and instead running model simulations in a continuous manner through the entire data set, it becomes possible to ensure that the data used for calibration and evaluation come from statistically similar distributions, thereby improving the quality of the calibrated model. An additional benefit is that the model states need only be initialized once at the outset of the simulation. An important feature of our strategy is that, by removing the requirement for that calibration and evaluation data to consist of temporally consecutive observations, a very large number of data partitions can be achieved, which significantly improves the possibility of ensuring distributional similarity of the two subsets, thereby improving robustness and generalization ability of the model. Key Points: A novel strategy is proposed to calibrate and validate CRR models by discarding the use of time‐consecutive dataThe proposed approach achieves robust and transferable CRR model performance based on results from 163 catchmentsThe proposed method is generally applicable to all physics‐based models with potential to significantly improve model prediction confidence [ABSTRACT FROM AUTHOR]

Current data allocation algorithms neglect the problems of unsatisfactory allocation results and long execution time caused by the redundancy of full comparative data and the complexity of data types. To solve these problems, a real-time allocation algorithm of full comparison data based on storm is proposed. Firstly, the phase unwrapping algorithm of minimum spanning tree is used to remove redundant data in full comparison data; then, the distributed data clustering algorithm and storm framework are used to realize the full comparison data clustering after redundancy removal. Several main factors affecting the selection of statistical information are summarized according to the clustering results. Then the communication cost of data loading and transaction processing is determined, and the trade-off between read-only transaction and update transaction cost is achieved. By judging whether the total cost of read-only transaction and update transaction is reduced or not, the replica is eliminated, and a full comparison data allocation algorithm with minimum total cost of read-only transaction and update transaction is proposed to realize real-time allocation of full-comparative data. The example analysis shows that the proposed algorithm can meet the user's needs in terms of execution time, acceleration ratio, storage efficiency and cost. Compared with the reference algorithm, the proposed algorithm has the lowest execution time, the highest acceleration ratio and the closest allocation cost to the ideal overhead. [ABSTRACT FROM AUTHOR]

Abstract In Distributed Database Systems (DDBS), communication costs and response time have long been open-ended challenges. Nevertheless, when DDBS is carefully designed, the desired reduction in communication costs will be achieved. Data fragmentation (data clustering) and data allocation are on popularity as the prime strategies in constant use to design DDBS. Based on these strategies, on the other hand, several design techniques have been presented in the literature to improve DDBS performance using either empirical results or data statistics, making most of them imperfect or invalid particularly, at least, at the initial stage of DDBSs design. In this paper, thus, a heuristic k-means approach for vertical fragmentation and allocation is introduced. This approach is primarily focused on DDBS design at the initial stage. Many techniques are being joined in a step to make a promising work. A brief yet effective experimental study, on both artificially-created and real datasets, has been conducted to demonstrate the optimality of the proposed approach, comparing with its counterparts, as the obtained results has been shown encouraging.

Low-power and short-latency memory access is critical to the performance of chip multiprocessor (CMP) system devices, especially to bridge the performance gap between memory and CPU. Together with increased demand for low-energy consumption and high-speed memory, scratch-pad memory (SPM) has been widely adopted in multiprocessor systems. In this paper, we employ a hybrid SPM, composed of a static random-access memory and a nonvolatile memory (NVM), to replace the cache in CMP. However, there are several challenges related to the CMP that need to be addressed, including how to dynamically assign processors to application tasks and dynamically allocate data to memories. To solve these problems based on this architecture, we propose a novel dynamic data allocation and task scheduling algorithm, i.e., dynamic greedy data allocation and task scheduling (DGDATS). Experiments on DSP benchmarks demonstrate the effectiveness and efficiency of our proposed algorithms; namely, our proposed algorithm can generate a highly efficient dynamic data allocation and task scheduling approach to minimize the total execution cost and produce the least amount of write operations on NVMs. Our extensive simulation study demonstrates that our proposed algorithm exhibits an excellent performance compared with the heuristic allocation (HA) and adaptive genetic algorithm for data allocation (AGADA) algorithms. Based on the CMP systems with hybrid SPMs, DGDATS reduces the total execution cost by 22.18% and 51.37% compared with those of the HA and AGADA algorithms, respectively. Additionally, the average number of write operations on NVM is 19.82% lower than that of HA.

Energy consumption has become one of the main bottlenecks that limit the performance improvement of heterogeneous multiprocessor systems. In a heterogeneous distributed shared‐memory multiprocessor system (HDSMS), each processor can access all the memories, and each data can be stored in different memories. This article aims at addressing the problem of task scheduling and data allocation (TSDA) on HDSMS. To minimize the total energy consumption under a time constraint for TSDA, we propose two algorithms: the extended tree assignment for task scheduling incorporating data allocation (ETATS‐DA) and critical path task scheduling and data allocation (CPTSDA). The ETATS‐DA algorithm first utilizes the extended tree assignment to search the near optimal solution for task assignment, and then allocates data to memory based on the result of assignment. The CPTSDA algorithm considers TSDA jointly on a critical path simultaneously. Our proposed algorithms perform coherent data allocation under the consideration of best task scheduling by running two different heuristic strategies, respectively, and taking the best result as the final result. We conduct a large number of simulation experiments to test the performance of our algorithms, and the results validate the higher performance of our methods compared with the state‐of‐the‐art algorithms. [ABSTRACT FROM AUTHOR]

The production of lithium-ion batteries is highly complex and characterized by continuous as well as discrete material flows and processes. A first step towards controlling the complexity of battery production is to create transparency through data collection. Electrodes as one of the key elements in a battery cell play a decisive role for the battery performance. The allocation of production data to electrodes enables a detailed digital twin and an individual grading system. This paper presents a concept for the marking of electrode sheets and requirements on markers as well as on marking technologies due to boundary conditions. [ABSTRACT FROM AUTHOR]

With the complete application of artificial intelligence in the field of industrial production and manufacturing and the rapid development of edge computing, industrial processing sites often need to deploy machine learning tasks at edges and terminals. We propose a data allocation method based on Distributed Deep Neural Networks (DDNN) framework, which allocates data to edge servers or stays locally for processing. DDNN divides deep learning tasks and deploys pre-trained shallow neural networks and deep neural networks at local or edges, respectively. However, all data is processed locally, and the failure is sent to the edge server or the cloud. It will lead to excessive pressure on local terminal equipment and long-term idle edge servers, which cannot meet industrial production's real-time requirements on user privacy and time-sensitive tasks. In this paper, the complexity and inference error rate of machine learning model, the data processing speed of local equipment and edge server, and the transmission time are comprehensively considered to establish the system model. A joint optimization problem is proposed to minimize the total data processing delay. The optimal solution is derived analytically, and the optimal data allocation methhod is given. Simulation experiments are designed to verify the method's effectiveness and study the influence of key parameters on the allocation method. [ABSTRACT FROM AUTHOR]

The ever-increasing amount of RDF data made available requires data to be partitioned across multiple servers. We have witnessed some research progress made towards scaling RDF query processing based on suitable data distribution methods. In general, they work well for queries matching simple triple patterns, but they are not efficient for queries involving more complex patterns. In this paper, we present an RDF data distribution method which overcomes the shortcomings of the current approaches in order to scale RDF storage both on the volume of data and query processing. We apply a method that identifies frequent patterns accessed by queries in order to keep related data in the same partition. We deploy our reasoning on a summarized view of data in order to avoid exhaustive analysis on large datasets. As result, partitioning templates are obtained from data items in an RDF structure. In addition, we provide an approach for dynamic data insertions even if new data do not conform to the original RDF structure. Apart from the repartitioning approaches, we use an overflow repository to store data which may not follow the original schema. Our study shows that our method scales well and is effective to improve the overall performance by decreasing the amount of message passing among servers, compared to alternative data distribution approaches for RDF. [ABSTRACT FROM AUTHOR]

In NAND flash memory-based solid-state drives (SSDs), during the idle time between the consecutive program/erase cycles (dwell time), the dielectric damage of flash cell can be partially repaired, also known as the self-recovery effect. As the effectiveness of the self-recovery effect can be improved under high temperature, self-healing SSDs are proven feasible to extend the flash endurance significantly. However, current self-healing SSDs perform the heating operations on all the worn-out blocks without considering the data retention requirement, and measures the lifetime of flash memory based on the worst-case self-recovery effect, leading to some unnecessary heating operations and the degraded performance. We propose SmartHeating, a smart heating scheme that exploits the dwell time variation and the write hotness variation to improve the I/O performance and the lifetime of self-healing SSDs. SmartHeating tracks the dwell time of all worn-out flash blocks, predicts their self-recovery effect and reliability, and avoids performing heating operations on the worn-out flash blocks that still have strong flash reliability. In addition, by exploiting the data hotness variation, SmartHeating only heats the worn-out flash blocks that store write-cold data, while allocating write-hot data to a small portion of worn-out flash blocks with negligible refresh overhead. The experimental results show that SmartHeating reduces the number of heating operations by 12.5% on average, boosts I/O performance of flash storage systems by 21.0%, and improves the lifetime of flash memory by $1.20\times $ compared with conventional heating scheme. [ABSTRACT FROM AUTHOR]

In the literature of distributed database system (DDBS), several methods sought to meet the satisfactory reduction on transmission cost (TC) and were seen substantially effective. Data Fragmentation, site clustering, and data distribution have been considered the major leading TC-mitigating influencers. Sites clustering, on one hand, aims at grouping sites appropriately according to certain similarity metrics. On the other hand, data distribution seeks to allocate the fragmented data into clusters/sites properly. The combination of these methods, however, has been shown fruitful concerning TC reduction along with network overheads. In this work, hence, a heuristic clustering-based approach for vertical fragmentation and data allocation is meticulously designed. The focus is directed on proposing an influential solution for improving relational DDBS throughputs across an aggregated similarity-based fragmentation procedure, an effective site clustering and a greedy algorithm-driven data allocation model. Moreover, the data replication is also considered so TC is further minimized. Through the delineated-below evaluation, the findings of experimental implementation have been observed to be promising.

Scratchpad memory (SPM) is widely utilized in many embedded systems as a software-controlled on-chip memory to replace the traditional cache. New non-volatile memory (NVM) has emerged as a promising candidate to replace SRAM in SPM, due to its significant benefits, such as low-power consumption and high performance. In particular, several representative NVMs, such as PCM, ReRAM, and STT-RAM can build multiple-level cells (MLC) to achieve even higher density. Nevertheless, this triggers off higher energy overhead and longer access latency compared with its single-level cell (SLC) counterpart. To address this issue, this paper first proposes a specific SPM with morphable NVM, in which the memory cell can be dynamically programmed to the MLC mode or SLC mode. Considering the benefits of high-density MLC and low-energy SLC, a simple and novel optimization technique, named theory of thermal expansion and contraction, is presented to minimize the energy consumption and access latency in embedded systems. The basic idea is to dynamically adjust the size configure of SLC/MLC in SPM according to the different workloads of program and allocate the optimal storage medium for each data. Therefore, an integer linear programming formulation is first built to produce an optimal SLC/MLC SPM partition and data allocation. In addition, a corresponding approximation algorithm is proposed to achieve near-optimal results in polynomial time. Finally, the experimental results show that the proposed technique can effectively improve the system performance and reduce the energy consumption.

Customers often suffer from the variability of data access time in (edge) cloud storage service, caused by network congestion, load dynamics, and so on. One efficient solution to guarantee a reliable latency-sensitive service (e.g., for industrial Internet of Things application) is to issue requests with multiple download/upload sessions which access the required data (replicas) stored in one or more servers, and use the earliest response from those sessions. In order to minimize the total storage costs, how to optimally allocate data in a minimum number of servers without violating latency guarantees remains to be a crucial issue for the cloud provider to deal with. In this paper, we study the latency-sensitive data allocation problem, the latency-sensitive data reallocation problem and the latency-sensitive workload consolidation problem for cloud storage. We model the data access time as a given distribution whose cumulative density function is known, and prove that these three problems are NP-hard. To solve them, we propose an exact integer nonlinear program (INLP) and a Tabu Search-based heuristic. The simulation results reveal that the INLP can always achieve the best performance in terms of lower number of used nodes and higher storage and throughput utilization, but this comes at the expense of much higher running time. The Tabu Search based heuristic, on the other hand, can obtain close-to-optimal performance, but in a much lower running time.

The production of Lithium-Ion Battery (LIB) cells is characterized by the interlinking of different production processes with a manifold of intermediate products. To be able to ensure high quality and enable a traceability of different production and product characteristics (e.g. energy consumption, material), a tracking and tracing concept is required. In this paper, a practical tracking and tracing concept throughout the production of LIB cells, enabling inline data-driven applications, is introduced. As a part of this concept an intelligent tracking and tracing platform is shown, which allows the generation of a pre-clustered data sets to facilitate future data-driven applications. [ABSTRACT FROM AUTHOR]

In Distributed Database Systems (DDBS), communication costs and response time have long been open-ended challenges. Nevertheless, when DDBS is carefully designed, the desired reduction in communication costs will be achieved. Data fragmentation (data clustering) and data allocation are on popularity as the prime strategies in constant use to design DDBS. Based on these strategies, on the other hand, several design techniques have been presented in the literature to improve DDBS performance using either empirical results or data statistics, making most of them imperfect or invalid particularly, at least, at the initial stage of DDBSs design. In this paper, thus, a heuristic k-means approach for vertical fragmentation and allocation is introduced. This approach is primarily focused on DDBS design at the initial stage. Many techniques are being joined in a step to make a promising work. A brief yet effective experimental study, on both artificially-created and real datasets, has been conducted to demonstrate the optimality of the proposed approach, comparing with its counterparts, as the obtained results has been shown encouraging. [ABSTRACT FROM AUTHOR]

In order to reduce the effect of the packet disorder on the transmission performance at the receiver in the CMT system, this paper developed a new data allocation scheme in the transmission terminal. The scheme predicted the packet forward transmission delay based on the bandwidth, round trip transmission delay and congestion window of the path, and used it as a measure factor of the path transmission priority in the CMT system. The sender assigned packets in the queue for each path based on the path transmission priority and sender cache status, which did not cause the receiver to be out of order. Simulation results show that compared with the round-robin and ATLB algorithm, the proposed data allocation scheme can reduce the number of disorder data apparently. [ABSTRACT FROM AUTHOR]

As massive volumes of Resource Description Framework (RDF) data are growing, designing a distributed RDF database system to manage them is necessary. In designing this system, it is very common to partition the RDF data into some parts, called fragments, which are then distributed. Thus, the distribution design comprises two steps: fragmentation and allocation. In this study, we explore the workload for fragmentation and allocation, which aims to reduce the communication cost during SPARQL query processing. Specifically, we adaptively maintain some frequent access patterns (FAPs) to reflect the characteristics of the workload while ensuring the data integrity and approximation ratio. Based on these frequent access patterns, we propose three fragmentation strategies, namely vertical, horizontal, and mixed fragmentation, to divide RDF graphs while meeting different types of query processing objectives. After fragmentation, we discuss how to allocate these fragments to various sites while balancing the fragments. Finally, we discuss how to process queries based on the results of fragmentation and allocation. Experiments over large RDF datasets confirm the superior performance of our proposed solutions. [ABSTRACT FROM AUTHOR]

Thanks to the advance of cloud computing technologies, users can access the data stored at cloud data centers at any time and from any where. However, the data centers are usually sparsely distributed over the Internet and are far away from end users. In this paper, we consider to construct a cache network by a large number of cache nodes close to the end users in order to minimize the data access delay.We firstly formulate the problem of placing the replicas of data items to cache nodes as a mixed integer programming (MIP) problem. Then, we proposed an efficient heuristic algorithm that allocates at least one replica of each data item in the cache network and attempt to allocate more data items so as to minimize the total data access cost. The simulation results show that our proposed algorithm behaves much better than a well-known LRU algorithm and the computation complexity is limited.

There are two key issues in distributed intrusion detection system, that is, maintaining load balance of system and protecting data integrity. To address these issues, this paper proposes a new distributed intrusion detection model for big data based on nondestructive partitioning and balanced allocation. A data allocation strategy based on capacity and workload is introduced to achieve local load balance, and a dynamic load adjustment strategy is adopted to maintain global load balance of cluster. Moreover, data integrity is protected by using session reassemble and session partitioning. The simulation results show that the new model enjoys favorable advantages such as good load balance, higher detection rate and detection efficiency. [ABSTRACT FROM AUTHOR]

Abstract Methodological handling of queries is a crucial requirement in social networks connected to a graph NoSQL database that incorporates massive amounts of data. The massive data need to be partitioned across numerous nodes so that the queries when executed can be retrieved from a parallel structure. A novel storage mechanism for effective query processing must to be established in graph databases for minimizing time overhead. This paper proposes a metaheuristic algorithm for partitioning of graph database across nodes by placement of all related information on same or adjacent nodes. The graph database allocation problem is proved to be NP-Hard. A metaheuristic algorithm comprising of Best Fit Decreasing with Ant Colony Optimization is proposed for data allocation in a distributed architecture of graph NoSQL databases. Lucene index is applied on proposed allocation for faster query processing. The proposed algorithm with Lucene is evaluated based on simulation results obtained from different heuristics available in literature. [ABSTRACT FROM AUTHOR]

In the article, a novel bitrate adaptation method for data streams allocation in heterogeneous Wireless Body Area Networks (WBANs) is presented. The efficiency of the proposed algorithm was compared with other known algorithms of data stream allocation using computer simulation. A dedicated simulator has been developed using results of measurements in the real environment. The usage of the proposed adaptive data streams allocation method by transmission rate adaptation based on radio channel parameters can increase the efficiency of resources’ usage in a heterogeneous WBANs, in relation to fixed bitrates transmissions and the use of well-known algorithms. This increase of efficiency has been shown regardless of the mobile node placement on the human body. [ABSTRACT FROM AUTHOR]

Large amount of data from social networks needs to be shared, distributed and indexed in a parallel structure to be able to make best use of the data. Neo4j High Availability (Neo4jHA) is a popular open-source graph database used for query handling on large social data. This paper analyses how storing and indexing of social data across machines can be carried out by placing all the related information on the same or adjacent machines, with replication. The social graph data allocation problem referred to as Neo4jHA allocation has proved to be NP-Hard in this paper. An integration of Best Fit Decreasing algorithm with Ant Colony Optimization based metaheuristics is proposed for data allocation in a distributed architecture of Neo4jHA. The evaluation of the algorithm is carried out by simulation. The query processing efficiency is compared with other heuristic algorithms like First Fit, Best Fit, First Fit Decreasing and Best Fit Decreasing found in literature. A Skip List index was constructed on Neo4jHA of every machine after the implementation of the proposed allocation strategy for enhancing the query processing efficiency. The results illustrate how the proposed algorithm outperforms other data allocation approaches in query execution with and without an index. [ABSTRACT FROM AUTHOR]

Abstract: Hydrological models are used for a wide variety of engineering purposes, including streamflow forecasting and flood‐risk estimation. To develop such models, it is common to allocate the available data to calibration and evaluation data subsets. Surprisingly, the issue of how this allocation can affect model evaluation performance has been largely ignored in the research literature. This paper discusses the evaluation performance bias that can arise from how available data are allocated to calibration and evaluation subsets. As a first step to assessing this issue in a statistically rigorous fashion, we present a comprehensive investigation of the influence of data allocation on the development of data‐driven artificial neural network (ANN) models of streamflow. Four well‐known formal data splitting methods are applied to 754 catchments from Australia and the U.S. to develop 902,483 ANN models. Results clearly show that the choice of the method used for data allocation has a significant impact on model performance, particularly for runoff data that are more highly skewed, highlighting the importance of considering the impact of data splitting when developing hydrological models. The statistical behavior of the data splitting methods investigated is discussed and guidance is offered on the selection of the most appropriate data splitting methods to achieve representative evaluation performance for streamflow data with different statistical properties. Although our results are obtained for data‐driven models, they highlight the fact that this issue is likely to have a significant impact on all types of hydrological models, especially conceptual rainfall‐runoff models. [ABSTRACT FROM AUTHOR]