Opportunistic prioritised clustering framework for improving OODBMS performance

We propose a dynamic clustering algorithm for maximizing a coordination gain in the uplink coordinated system. The dynamic clustering algorithm configures clusters periodically to adopt the change of the channel environment. Since the greedy-search clustering algorithm does not guarantee cell-edge users' performance and the full-search clustering algorithm (FSCA) is highly complex, we define a coordination gain between the coordinated communication system and the single-cell communication system, as a new parameter, to maximize the benefit of the coordinated communication system. We develop a maximum coordination gain (MAX-CG) clustering algorithm to maximize the coordination gain and an interference weight (IW) clustering algorithm to reduce complexity and guarantee the data rate of cell-edge users. Simulation results show that the MAX-CG clustering algorithm improves the average user rate and the 5% edge user rate. The IW clustering algorithm improves the 5% edge user's rate, guarantees the fairness among the cells, and reduces the complexity to only half of the existing algorithm.

Community detection is of great importance for online social network analysis. The volume, variety and velocity of data generated by today's online social networks are advancing the way researchers analyze those networks. For instance, real-world networks, such as Facebook, LinkedIn and Twitter, are inherently growing rapidly and expanding aggressively over time. However, most of the studies so far have been focusing on detecting communities on the static networks. It is computationally expensive to directly employ a well-studied static algorithm repeatedly on the network snapshots of the dynamic networks. We propose DynaMo, a novel modularity-based dynamic community detection algorithm, aiming to detect communities of dynamic networks as effective as repeatedly applying static algorithms but in a more efficient way. DynaMo is an adaptive and incremental algorithm, which is designed for incrementally maximizing the modularity gain while updating the community structure of dynamic networks. In the experimental evaluation, a comprehensive comparison has been made among DynaMo, Louvain (static) and 5 other dynamic algorithms. Extensive experiments have been conducted on 6 real-world networks and 10,000 synthetic networks. Our results show that DynaMo outperforms all the other 5 dynamic algorithms in terms of the effectiveness, and is 2 to 5 times (by average) faster than Louvain algorithm.

A dynamic yard space reservation algorithm based on reward-penalty mechanism

Suboptimal device programming is frequent in non-responders to cardiac resynchronization therapy (CRT). However, the role of device optimization and the most appropriate technique are still unknown. The aim of our study was to analyse the effect of different CRT optimization techniques within a network meta-analysis. A systematic search was conducted on MEDLINE, Embase and CENTRAL for studies comparing outcomes with empirical device settings or optimization using echocardiography, static algorithms or dynamic algorithms. Studies investigating the effect of optimization in non-responders were also analysed. A total of 17 studies with 4346 patients were included in the quantitative analysis. Of the treatments and outcomes examined, a significant difference was found only between dynamic algorithms and echocardiography, with the former leading to a higher echocardiographic response rate [odds ratio (OR): 2.02, 95% confidence interval (CI) 1.21-3.35], lower heart failure hospitalization rate (OR: 0.75, 95% CI 0.57-0.99) and greater improvement in 6-minute walk test [mean difference (MD): 45.52m, 95% credible interval (CrI) 3.91-82.44m]. We found no significant difference between empirical settings, static algorithms and dynamic algorithms. Seven studies with 228 patients reported response rates after optimization in non-responders. Altogether, 34.3%-66.7% of initial non-responders showed improvement after optimization, depending on response criteria. At the time of CRT implantation, dynamic algorithms may serve as a resource-friendly alternative to echocardiographic optimization, with similar or better mid-term outcomes. However, their superiority over empirical device settings needs to be investigated in further trials. For non-responders, CRT optimization should be considered, as the majority of patients experience improvement.

Dynamic and static algorithms for optimal placement of resources in a tree

We investigate how static store-and-forward routing algorithms can be transformed into efficient dynamic algorithms, that is, how algorithms that have been designed for the case that all packets are injected at the same time can be adapted to more realistic scenarios in which packets are continuously injected into the network. Besides describing specific transformations for well-known static routing algorithms, we present a black box transformation scheme applicable to every static, oblivious routing algorithm. We analyze the performance of our protocols under a stochastic and an adversarial model of packet injections. One result of our specific transformations is the first dynamic routing algorithm for leveled networks that is stable for arbitrary admissible injection rates and that works with packet buffers of size depending solely on the injection rate and the node degree, but not on the size of the network. Furthermore, we prove strong delay bounds for the packets. Our results imply, for example, that a throughput of 99% can be achieved on an n-input butterfly network with buffers of constant size while each packet is delivered in time O(log n), with high probability. Our black box transformation ensures that if the static algorithm is pure (i.e., no extra packets apart from the original packets are routed), its dynamic variant is stable up to a maximum possible injection rate. Furthermore, in the stochastic model, the routing time of a packet depends on local parameters such as the length of its routing path, rather than on the maximum possible path length, even if the static algorithm chosen for the transformation does not provide this locality feature and is not pure. In the adversarial model, the delay bound of the packets is closely related to the time bound given for the static algorithm.

We investigate how static store-and-forward routing algorithms can be transformed into efficient dynamic algorithms, that is, how algorithms that have been designed for the case that all packets are injected at the same time can be adapted to more realistic scenarios in which packets are continuously injected into the network. Besides describing specific transformations for well-known static routing algorithms, we present a black box transformation scheme applicable to every static, oblivious routing algorithm. We analyze the performance of our protocols under a stochastic and an adversarial model of packet injections. One result of our specific transformations is the first dynamic routing algorithm for leveled networks that is stable for arbitrary admissible injection rates and that works with packet buffers of size depending solely on the injection rate and the node degree, but not on the size of the network. Furthermore, we prove strong delay bounds for the packets. Our results imply, for example, that a throughput of 99% can be achieved on an n-input butterfly network with buffers of constant size while each packet is delivered in time O(log n), with high probability. Our black box transformation ensures that if the static algorithm is pure (i.e., no extra packets apart from the original packets are routed), its dynamic variant is stable up to a maximum possible injection rate. Furthermore, in the stochastic model, the routing time of a packet depends on local parameters such as the length of its routing path, rather than on the maximum possible path length, even if the static algorithm chosen for the transformation does not provide this locality feature and is not pure. In the adversarial model, the delay bound of the packets is closely related to the time bound given for the static algorithm.

With development of Internet of Things, wireless sensor networks (WSN) become larger and larger and if the present clustering and routing methods are widely used in large scale WSN, “energy hole” problem will deteriorate. To alleviate “energy hole” problem of WSN, a dynamic broadcast radius-based clustering and routing algorithm (DBRCR) is proposed. The algorithm calculates node broadcast radius every round, and based on the broadcast radius the neighbor table is established. Then according to the degrees of nodes, construct clusters iteratively. Finally, finding intra-cluster communication path based on data transmission success rate and routing for the inter-cluster communication based on nodes' residual energy and transmission energy consumption to minimize the data packet-loss rate and prolong the network lifetime.

A mixed attributes oriented dynamic SOM fuzzy cluster algorithm for mobile user classification

Wireless sensor networks have enticed lot of spotlight from researchers all around globe, owing to its wide applications in industrial, military and agricultural fields. Energy conservation and node deployment strategies play a paramount role for effective execution of Wireless Sensor Networks. Clustering of nodes in the wireless sensor networks is an approach commenced to achieve energy efficiency in the network. Clustering algorithm, if not executed properly can reduce life of the network. In this paper, a Two -Step Uniform Clustering (TSUC) algorithm has been proposed with the aim to provide connectivity to the nodes in every part of the network. This algorithm increases networks lifetime and throughput by re-clustering isolated nodes rather than providing them connectivity by already connected node. Results obtained after simulation showed that proposed TSUC algorithm performed better than the other existing clustering algorithm.

In this paper, we present and evaluate dynamic dictionary-based estimation methods for joint model order and parameter estimation. In dictionary-based estimation, a continuous parameter space is discretized, and vector-valued dictionary elements are formed for specific parameter values. A linear combination of a subset of dictionary elements is used to represent the model, where the number of elements used is the estimated model order, and the parameters corresponding to the selected elements are the parameter estimates. In static-based methods, the dictionary is fixed; while in the dynamic methods proposed here, the parameter sampling, and hence the dictionary, adapt to the data. We propose two dynamic dictionary-based estimation algorithms in which the dictionary elements are dynamically adjusted to improve parameter estimation performance. We examine the performance of both static and dynamic algorithms in terms of probability of correct model order selection and the root mean-squared error of parameter estimates. We show that dynamic dictionary methods overcome the problem of estimation bias induced by quantization effects in static dictionary-based estimation, and we demonstrate that dictionary-based estimation methods are capable of parameter estimation performance comparable to the Cramér-Rao lower bound and to traditional ML-based model estimation over a wide range of signal-to-noise ratios.

In this paper we propose novel energy-aware task-mapping algorithms -static and dynamic, for body area networks (BANs). These algorithms map tasks onto a heterogeneous wireless system of sensor nodes such that the tasks meet the deadline and also the energy consumption is a minimum. The static mapping algorithm uses the max-flow min-cut algorithm to obtain an efficient initial mapping. Then the dynamic algorithm uses the scaling factor criteria to improve the mapping and works in an iterative fashion to improve the quality of mapping. We illustrate the workings of these algorithms via examples for the ease of understanding.

针对数据流的聚类算法，近年来取得了有效的进展，出现了许多卓有成效的算法。随着信息采集技术的进步，需要处理的数据量越来越大，需要研究针对数据流的并行聚类算法。本文基于串行的数据流聚类算法D-Stream作出并行化改进，用通用的大数据处理框架Spark设计了一个基于分布式架构运行的动态数据聚类算法PDStream。实验结果表明，该算法具有更高的效率和良好的扩展性，能够实现分布式架构下的流数据动态聚类。 In the era of big data, with the rapid growth of data size, the requirements of data processing in-crease constantly. It has put forward many effective algorithms for data stream clustering these years. However, with the continuous development of social technology, single machine environ-ment has been difficult to meet the needs of data mining. Cluster environment is used more for information collection and data processing, the traditional clustering algorithm does not adapt well to the new processing requirements. This paper made some improvements from the data stream clustering algorithm D-Stream, used the big data processing framework Spark and designed a dynamic data clustering algorithm PDStream based on distributed architecture. The new algorithm is proved to be more efficient and able to perform dynamic clustering tasks under distributed architecture from the results of experiment.

Along with the development of big data, knowledge updating occurs in various situations, some scholars had studied the dynamic method of updating knowledge in multigranulation decision-theoretic rough sets when adding or deleting granular structures. However, there is no study about the case of adding or deleting objects, which limits the development of multigranulation decision-theoretic rough sets. Based on the matrix method, the dynamic knowledge updating of optimistic multigranulation decision-theoretic rough set and pessimistic multigranulation decision-theoretic rough set were studied in this paper, then given the static algorithm and dynamic algorithms, and the time complexity of three algorithms was analyzed. The theory and experimental results show that two dynamic algorithms are both more effective than the static algorithm.

A (fully) dynamic graph algorithm is a data structure that supports edge insertions, edge deletions, and answers certain queries that are specific to the problem under consideration. There has been a lot of research on dynamic algorithms for graph problems that are solvable in polynomial time by a static algorithm. However, while there is a large body of theoretical work on efficient dynamic graph algorithms, a lot of these algorithms were never implemented and empirically evaluated.In this work, we consider the fully dynamic edge orientation problem, also called fully dynamic Δ-orientation problem, which is to maintain an orientation of the edges of an undirected graph such that the out-degree is low. If edges are inserted or deleted, one may have to flip the orientation of some edges in order to avoid vertices having a large out-degree. While there has been theoretical work on dynamic versions of this problem, currently there is no experimental evaluation available. In this work, we close this gap and engineer a range of new dynamic edge orientation algorithms as well as algorithms from the current literature. The most successful newly proposed algorithms in this paper are based on repeatedly swapping edges with smallest degree neighbors and based on breadth-first search. Moreover, we evaluate these algorithms on real-world dynamic graphs. Generally, these algorithms outperform the algorithms currently proposed in the literature. The best algorithm considered in this paper in terms of quality, based on a simple breadth-first search, computes the optimum result more than 90% of the instances and is on average only 2.4% worse than the optimum solution.*The full version of the paper can be accessed at https://arxiv.org/abs/2301.06968