Big Data Systems Davoudian, Ali; Liu, Mengchi
ACM computing surveys,
09/2021, Volume:
53, Issue:
5
Journal Article
Peer reviewed
Big Data Systems (BDSs) are an emerging class of scalable software technologies whereby massive amounts of heterogeneous data are gathered from multiple sources, managed, analyzed (in batch, stream ...or hybrid fashion), and served to end-users and external applications. Such systems pose specific challenges in all phases of software development lifecycle and might become very complex by evolving data, technologies, and target value over time. Consequently, many organizations and enterprises have found it difficult to adopt BDSs. In this article, we provide insight into three major activities of software engineering in the context of BDSs as well as the choices made to tackle them regarding state-of-the-art research and industry efforts. These activities include the engineering of requirements, designing and constructing software to meet the specified requirements, and software/data quality assurance. We also disclose some open challenges of developing effective BDSs, which need attention from both researchers and practitioners.
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IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
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.
Recent demands for storing and querying big data have revealed various shortcomings of traditional relational database systems. This, in turn, has led to the emergence of a new kind of complementary ...nonrelational data store, named as NoSQL. This survey mainly aims at elucidating the design decisions of NoSQL stores with regard to the four nonorthogonal design principles of distributed database systems: data model, consistency model, data partitioning, and the CAP theorem. For each principle, its available strategies and corresponding features, strengths, and drawbacks are explained. Furthermore, various implementations of each strategy are exemplified and crystallized through a collection of representative academic and industrial NoSQL technologies. Finally, we disclose some existing challenges in developing effective NoSQL stores, which need attention of the research community, application designers, and architects.
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IZUM, KILJ, NUK, PILJ, SAZU, UL, UM, UPUK
Abstract
Broad-spectrum absorption and highly effective charge-carrier separation are two essential requirements to improve the photocatalytic performance of semiconductor-based photocatalysts. In ...this work, a fascinating one-photon system is reported by rationally fabricating 2D in-plane Bi
2
O
3
/BiOCl (i-Cl) heterostructures for efficient photocatalytic degradation of RhB and TC. Systematic investigations revealed that the matched band structure generated an internal electric field and a chemical bond connection between the Bi
2
O
3
and BiOCl in the Bi
2
O
3
/BiOCl composite that could effectively improve the utilization ratio of visible light and the separation effectivity of photo-generated carriers in space. The formed interactions at the 2D in-plane heterojunction interface induced the one-photon excitation pathway which has been confirmed by the experiment and DFT calculations. As a result, the i-Cl samples showed significantly enhanced photocatalytic efficiency towards the degradation of RhB and TC (RhB: 0.106 min
−1
; TC: 0.048 min
−1
) under visible light. The degradation activities of RhB and TC for i-Cl were 265.08 and 4.08 times that of pure BiOCl, as well as 9.27 and 2.14 times that of mechanistically mixed Bi
2
O
3
/BiOCl samples, respectively. This work provides a logical strategy to construct other 2D in-plane heterojunctions with a one-photon excitation pathway with enhanced performance.
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.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
High utility itemsets refer to the sets of items with high utility like profit in a database, and efficient mining of high utility itemsets plays a crucial role in many real-life applications and is ...an important research issue in data mining area. To identify high utility itemsets, most existing algorithms first generate candidate itemsets by overestimating their utilities, and subsequently compute the exact utilities of these candidates. These algorithms incur the problem that a very large number of candidates are generated, but most of the candidates are found out to be not high utility after their exact utilities are computed. In this paper, we propose an algorithm, called HUI-Miner (High Utility Itemset Miner), for high utility itemset mining. HUI-Miner uses a novel structure, called utility-list, to store both the utility information about an itemset and the heuristic information for pruning the search space of HUI-Miner. By avoiding the costly generation and utility computation of numerous candidate itemsets, HUI-Miner can efficiently mine high utility itemsets from the utility-lists constructed from a mined database. We compared HUI-Miner with the state-of-the-art algorithms on various databases, and experimental results show that HUI-Miner outperforms these algorithms in terms of both running time and memory consumption.
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
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