Streaming graph partitioning methods have recently gained attention due to their ability to scale to very large graphs with limited resources. However, many such methods do not consider workload and ...graph characteristics. This may degrade the performance of queries by increasing inter-node communication and computational load imbalance. Moreover, existing workload-aware methods cannot consistently provide good performance as they do not consider dynamic workloads that keep emerging in graph applications. We address these issues by proposing a novel workload-adaptive streaming partitioner named WASP, that aims to achieve low-latency and high-throughput online graph queries. As each workload typically contains frequent query patterns, WASP exploits the existing workload to capture active vertices and edges which are frequently visited and traversed, respectively. This information is used to heuristically improve the quality of partitions either by avoiding the concentration of active vertices in a few partitions proportional to their visit frequencies or by reducing the probability of the cut of active edges proportional to their traversal frequencies. In order to assess the impact of WASP on a graph store and to show how easily the approach can be plugged on top of the system, we exploit it in a distributed graph-based RDF store. Our experiments over three synthetic and real-world graph datasets and the corresponding static and dynamic query workloads show that WASP achieves a better query performance against state-of-the-art graph partitioners, especially in dynamic query workloads.
The prevalence of e-learning systems has made educational resources more accessible, interactive and effective to learners without the geographic and temporal boundaries. However, as the number of ...users increases and the volume of data grows, current e-learning systems face some technical and pedagogical challenges. This paper provides a comprehensive review on the efforts of applying new information and communication technologies to improve e-learning services. We first systematically investigate current e-learning systems in terms of their classification, architecture, functions, challenges, and current trends. We then present a general architecture for big data based e-learning systems to meet the ever-growing demand for e-learning. We also describe how to use data generated in big data based e-learning systems to support more flexible and customized course delivery and personalized learning.
Entity and relation extraction for Chinese texts are typical performed in a pipelined fashion in the sense that by first segmenting sequence into words then recognizing entities and relations ...subsequently. However, this process often leads to the problem of error propagation and prevents cross-task information integration. To address this issue, we propose a novel transition-based model that performs nested entity recognition and relation extraction jointly without the need of word segmentation beforehand, which is achieved by leveraging the recent advance in using a lattice structure for Chinese sentence encodings. On standard ACE benchmarks, our model gives the best results in the literature. Further analyses show the effectiveness of the proposed architecture in capturing structural outputs.
•We propose a memory efficient method, CMC-bit, for maximal clique enumeration on real-world sparse graphs.•We use an efficient order strategy to reduce both the CPU cost in the candidate map ...construction and to decrease the candidate clique number.•The method has a stable total execution time and suits well in memory-limited scenarios.•We devise a parallel implementation of CMC-bit based on MapReduce.•The method is faster than baselines on real world datasets and its parallel implementation is scalable.
Maximal clique enumeration (MCE) is a widely studied problem that plays a crucial role in structure mining of undirected graphs. The increasing scale of real-world graphs has brought the challenges of high memory cost and high CPU workload to the problem. In this paper, we propose a memory efficient method named CMC-bit for MCE on sparse graphs. It reduces the memory cost via minimizing the candidate cliques and representing them by the data structure bitset. It generates an appropriate order for the vertex set according to two optimized principles to reduce the CPU cost. We further design a partition-based CMC-bit algorithm with a one-side extending strategy to solve the memory-limited problem. We parallelize the CMC-bit algorithm based on MapReduce with a range-based partition strategy to make an optimal trade-off between the shuffling workload of graph decomposition and load balance in the Reduce phase. We conduct extensive experiments on 30 real-world datasets. The results demonstrate that both the CMC-bit algorithm and its parallel implementation significantly outperform the respective state-of-the-art algorithms in speed. We also show that the parallel CMC-bit algorithm achieves good performance on the scalability with respect to both the reducer number and the CPU number.
Due to the scalability and availability problems with traditional relational database systems, a variety of NoSQL stores have emerged over the last decade to deal with big data. How data are ...structured in a NoSQL store has a large impact on the query and update performance and the storage usage. Thus, different from the traditional database design, not only the data structure but also the data access patterns need to be considered in the design of NoSQL database schemas. In this paper, we present a general workload-driven method for designing key-value, wide-column, and document NoSQL database schemas. We first present a generic logical model Query Path Graph (QPG) that can represent the data structures of the UML class diagram. We also define mappings from the SQL-based query patterns to QPG and from QPG to aggregate-oriented NoSQL schemas. We use a cost model to measure the query and update performance and optimize the QPG schemas. We evaluate the proposed method with several typical case studies by simulating workloads on databases with different schema designs. The results demonstrate that our method preserves the generality and the quality of the design.
In current gait recognition methods, researchers predominantly focus on gait information at specific spatial scales, with a tendency to overlook information variances across different scales. ...Additionally, from observation, we found variations in the spatiotemporal information offered by different human body parts. To address these issues, we present a Multi-scale Network for Gait Recognition (GMSN), which aims to highlight key body parts in a more discriminative gait representation. GMSN consists of two key modules: the Multi-scale Feature Extractor (MSFE) and the Part-based Horizontal Mapping (PHM). MSFE employs multi-scale parallel convolutional networks to comprehensively learn features across different scales, capturing both local details and global information for an enriched representation of gait. Meanwhile, PHM focuses on enhancing the learning of crucial body parts that provide clear contours and movement patterns. Experiments on three public datasets demonstrate that our approach attains state-of-the-art recognition accuracy. On the CASIA-B dataset, our model achieves rank-1 accuracies of 98.2 %, 96.0 %, and 87.0 % under normal walking, bag-carrying, and coat-wearing conditions, respectively. On the OU-MVLP and GREW datasets, it achieves a rank-1 accuracy of 90.4 % and 50.6 %, respectively. Also, it can achieve relatively stable results when adding square occlusions to the test samples.
The semantic gap between keyword queries and search intents behind them motivates intensive studies on keyword query interpretation, which aims to interpret a keyword query to structured queries ...(a.k.a. patterns) representing most possibly relevant search intents. However, there still lacks of study on an important issue: how to guarantee the patterns are "reliable", which means the structured queries can be evaluated as really existing results. In this paper, we regard the reliability as a new metric for ranking patterns, and present a keyword query interpretation approach to find both reliable and relevant pattern trees on an arbitrary summary graph of underlying data. Specifically, we first propose a reliability estimation model to measure how possibly a pattern tree can be evaluated as a nonempty result set by statistics under reasonable assumptions. Second, we develop constrained top-<inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="zhong-ieq1-3144001.gif"/> </inline-formula> search algorithms that guarantee to return the optimal pattern trees for a specific keyword query. Moreover, to improve the efficiency of online search, we also design elaborate indexes, search heuristics and pruning strategies. Lastly, we perform comprehensive experiments on two real-world datasets, DBpedia and Yago, with both QALD-9 queries and random queries. The observations indicate our approach improves the accuracy and overall quality of top-<inline-formula><tex-math notation="LaTeX">k</tex-math> <mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="zhong-ieq2-3144001.gif"/> </inline-formula> results significantly.
A maximal clique is one of the most fundamental dense substructures in an undirected graph, and maximal clique enumeration (MCE) plays an essential role in densely connected subgraphs discovering. ...Existing algorithms of maximal clique enumeration employ recursive iteration of adjacent nodes as guiding thought, which incurs high time complexity. In this paper, we propose a linear time algorithm, CM-Constructor (Candidate Map Constructor), for maximal clique enumeration in large sparse graphs which generates a novel data structure called candidate map as result. A candidate map holds not only all maximal cliques of an undirected graph but also some non-maximal cliques that can be easily discarded via an inverted clique tree. To the best of our knowledge, CM-Constructor is the first algorithm to tackle maximal clique enumeration problem utilizing linear procedure. It generates all maximal cliques without duplications for an undirected graph G=(V,E) within O(|E|) time.
Finding k-edge connected components (k-ECCs) is widely used to investigate the major cohesive structures within a graph. Traditional methods utilizing global minimum cut to split the graph ...iteratively are too slow to deal with graphs of a large size. Recently, approximate algorithms based on a decomposition framework have been proposed to speed up the process. However, these algorithms produce low-quality results in graphs containing vertex pairs that are k-connected but do not belong to a k-ECC. In this paper, we propose a decomposition framework that utilizes a local edge connectivity check method to obtain high-quality results in a shorter period. The proposed method utilizes an improved maximum flow algorithm to detect k-ECCs from local k-cores by merging more vertices in each iteration than existing methods. Moreover, a pruning strategy is applied in the decomposition framework to reduce unnecessary branches in the decomposition tree. Our experimental results on both synthetic and real graphs show that our proposed algorithm is faster than the state-of-the-art algorithms in most datasets and achieves effective results among the approximate algorithms with a 96% accuracy on our experimental datasets.
•An efficient local edge connectivity check based decomposition framework to detect k-ECCs with high accuracy of results.•A k-core based maximal merging and a pruning strategy for optimizing the algorithm processing time and the height of decomposition tree.•An improved augmenting path flow method to directly calculate the minimum cut of two vertices in the check procedure.•Experimental results show that the algorithm can detect k-ECCs with outstanding speed while producing high-quality results (96% accuracy).
As a crucial step of biological event extraction, event trigger identification has attracted much attention in recent years. Deep representation methods, which have the superiorities of less feature ...engineering and end-to-end training, show better performance than statistical methods. While most deep learning methods have been done on sentence-level event extraction, there are few works taking document context into account, losing potentially informative knowledge that is beneficial for trigger detection. In this paper, we propose a variational neural approach for biomedical event extraction, which can take advantage of latent topics underlying documents. By adopting a joint modeling manner of topics and events, our model is able to produce more meaningful and event-indicative words compare to prior topic models. In addition, we introduce a language model embeddings to capture context-dependent features. Experimental results show that our approach outperforms various baselines in a commonly used multi-level event extraction corpus.
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