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| 1 | Multi-view Clustering: A Survey显示文摘In the big data era, the data are generated from different sources or observed from different views. These data are referred to as multi-view data. Unleashing the power of knowledge in multi-view data is very important in big data mining and analysis. This calls for advanced techniques that consider the diversity of different views,while fusing these data. Multi-view Clustering(MvC) has attracted increasing attention in recent years by aiming to exploit complementary and consensus information across multiple views. This paper summarizes a large number of multi-view clustering algorithms, provides a taxonomy according to the mechanisms and principles involved, and classifies these algorithms into five categories, namely, co-training style algorithms, multi-kernel learning, multiview graph clustering, multi-view subspace clustering, and multi-task multi-view clustering. Therein, multi-view graph clustering is further categorized as graph-based, network-based, and spectral-based methods. Multi-view subspace clustering is further divided into subspace learning-based, and non-negative matrix factorization-based methods. This paper does not only introduce the mechanisms for each category of methods, but also gives a few examples for how these techniques are used. In addition, it lists some publically available multi-view datasets.Overall, this paper serves as an introductory text and survey for multi-view clustering. | Yan Yang Hao Wang | 2018 | Big Data Mining and Analytics2018，1，2： | 20 |
| 2 | Artificial intelligence:a survey on evolution,models,applications and future trends显示文摘Artificial intelligence(AI)is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies,such as graphic processing unit,Internet of Things,cloud computing,and the blockchain,in the new generation of big data and Industry 4.0.In this paper,we construct an extensive survey over the period 1961-2018 of AI and deep learning.The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI,from underlying mechanisms to practical applications,from fundamental algorithms to industrial achievements,from current status to future trends.Although there exist many issues toward AI,it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields. | Yang Lu | 2019 | Journal of Management Analytics2019，6，1： | 14 |
| 3 | DEEPEYE: Link Prediction in Dynamic Networks Based on Non-negative Matrix Factorization显示文摘A Non-negative Matrix Factorization(NMF)-based method is proposed to solve the link prediction problem in dynamic graphs. The method learns latent features from the temporal and topological structure of a dynamic network and can obtain higher prediction results. We present novel iterative rules to construct matrix factors that carry important network features and prove the convergence and correctness of these algorithms. Finally, we demonstrate how latent NMF features can express network dynamics efficiently rather than by static representation,thereby yielding better performance. The amalgamation of time and structural information makes the method achieve prediction results that are more accurate. Empirical results on real-world networks show that the proposed algorithm can achieve higher accuracy prediction results in dynamic networks in comparison to other algorithms. | Nahla Mohamed Ahmed Ling Chen Yulong Wang Bin Li Yun Li Wei Liu | 2018 | Big Data Mining and Analytics2018，1，1： | 11 |
| 4 | Relation Classification via Recurrent Neural Network with Attention and Tensor Layers显示文摘Relation classification is a crucial component in many Natural Language Processing(NLP) systems. In this paper, we propose a novel bidirectional recurrent neural network architecture(using Long Short-Term Memory,LSTM, cells) for relation classification, with an attention layer for organizing the context information on the word level and a tensor layer for detecting complex connections between two entities. The above two feature extraction operations are based on the LSTM networks and use their outputs. Our model allows end-to-end learning from the raw sentences in the dataset, without trimming or reconstructing them. Experiments on the SemEval-2010 Task 8dataset show that our model outperforms most state-of-the-art methods. | Runyan Zhang Fanrong Meng Yong Zhou Bing Liu | 2018 | Big Data Mining and Analytics2018，1，3： | 9 |
| 5 | How technological proximity affect collaborative innovation?An empirical study of China’s Beijing-Tianjin-Hebei region显示文摘Based on joint-innovation patent data from 2000 to 2016 in the Beijing-Tianjin-Hebei region of China,the purpose of this paper is to analyze how technological proximity affects university-industry collaborative innovation in the Beijing-Tianjin-Hebei region.We adopt a 1:1 matching design to conduct an empirical study.The results show that the effect of technological proximity on the formation of collaborative innovation displays an inverted U-shape,and geographical proximity and institutional proximity play a positive role of forming a tie.Geographical proximity and institutional proximity as a coordination mechanism,have negatively influenced the relationship between technological proximity and the formation of university-industry collaborative innovation.Furthermore,university strength improves the possibility of collaborative innovation.These findings contributed to the understanding of the relationship between technological proximity and collaborative innovation. | Hongjun Chen Fuji Xie | 2018 | Journal of Management Analytics2018，5，4： | 9 |
| 6 | Blockchain and the related issues:a review of current research topics显示文摘The blockchain represents emerging technologies and future trends.For the traditional social organization and mode of operation,the development of the blockchain is a revolution.As a decentralized infrastructure and distributed general ledger agreement,the blockchain presents us with a great opportunity to establish data security and trust for automation and intelligence development in the Internet of Things(IoT)and it creates a new un-centralized programmable smart ecosystem.Our research synthesizes and analyses extant articles that focus on blockchain-related perspectives which will potentially play an important role in sustainable development in the world.Blockchain applications and future directions always attract more attention.Blockchain technology provides strong scalability and interoperability between the intelligent and the physical worlds. | Yang Lu | 2018 | Journal of Management Analytics2018，5，4： | 8 |
| 7 | The Ferric Reducing Ability of Plasma (FRAP) as a Measure of “Antioxidant Power”: The FRAP Assay显示文摘 | Iris F.F. Benzie J.J. Strain | 1996 | Analytical Biochemistry1996，，1： | 8 |
| 8 | A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering显示文摘Due to the widespread availability of implicit feedback(e.g., clicks and purchases), some researchers have endeavored to design recommender systems based on implicit feedback. However, unlike explicit feedback,implicit feedback cannot directly reflect user preferences. Therefore, although more challenging, it is also more practical to use implicit feedback for recommender systems. Traditional collaborative filtering methods such as matrix factorization, which regards user preferences as a linear combination of user and item latent vectors, have limited learning capacities and suffer from data sparsity and the cold-start problem. To tackle these problems,some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. However, there is as yet no research combining collaborative filtering and contentbased recommendation with deep learning. In this paper, we propose a novel deep hybrid recommender system framework based on auto-encoders(DHA-RS) by integrating user and item side information to construct a hybrid recommender system and enhance performance. DHA-RS combines stacked denoising auto-encoders with neural collaborative filtering, which corresponds to the process of learning user and item features from auxiliary information to predict user preferences. Experiments performed on the real-world dataset reveal that DHA-RS performs better than state-of-the-art methods. | Yu Liu Shuai Wang M.Shahrukh Khan Jieyu He | 2018 | Big Data Mining and Analytics2018，1，3： | 7 |
| 9 | Optimal quality level,order quantity and selling price for the retailer in a two-level supply chain显示文摘For a classical order quantity/pricing problem,we present a geometric programming(GP)approach to find the optimal selling price,order quantity and quality level to maximize the profit for the retail firm.Traditional models such as EOQ are not able to handle the nonlinearity of costs and demand.We adopt the GP approach and make a proper transformation of the model so as to solve this classical problem and obtain the global optimal solution.In addition to the optimal solutions,we also perform a sensitivity analysis.The study shows once more that GP is an excellent approach when decision variables interact in a nonlinear,especially exponential manner. | Sihua Zhou Guohua Wan Pengzhu Zhang Yuan Li | 2014 | Journal of Management Analytics2014，1，3： | 6 |
| 10 | An EPQ model with variable production,probabilistic deterioration and partial backlogging under inflation显示文摘This paper develops an economic production quantity(EPQ)model under the effect of inflation and time value of money.The rate of replenishment is considered to be a variable and the generalized unit production cost function is formulated by incorporating several factors,such as raw material,labour,replenishment rate,advertisements and other factors of the manufacturing system.The selling price of a unit is determined by a mark-up over the production cost.We have considered three types of continuous probabilistic deterioration function,and also considered that the holding cost of the item per unit time is assumed to be an increasing linear function of time spent in storage.In addition,shortages are allowed and partially backlogged.This model aids in minimizing the total inventory cost by finding the optimal cycle length and the optimal production quantity.The optimal solution of the model is illustrated with the help of numerical examples. | M.Palanivel R.Uthayakumar | 2014 | Journal of Management Analytics2014，1，3： | 6 |
| 11 | Big Data Analytics for Healthcare Industry:Impact,Applications,and Tools显示文摘In recent years, huge amounts of structured, unstructured, and semi-structured data have been generated by various institutions around the world and, collectively, this heterogeneous data is referred to as big data. The health industry sector has been confronted by the need to manage the big data being produced by various sources,which are well known for producing high volumes of heterogeneous data. Various big-data analytics tools and techniques have been developed for handling these massive amounts of data, in the healthcare sector. In this paper, we discuss the impact of big data in healthcare, and various tools available in the Hadoop ecosystem for handling it. We also explore the conceptual architecture of big data analytics for healthcare which involves the data gathering history of different branches, the genome database, electronic health records, text/imagery, and clinical decisions support system. | Sunil Kumar Maninder Singh | 2019 | Big Data Mining and Analytics2019，2，1： | 6 |
| 12 | Healthcare data analytics:using a metadata annotation approach for integrating electronic hospital records显示文摘The data in electronic medical records(EMR)are complex in structure.They are independent,yet related to each other.In order to improve information access through the use of EMR,annotating work on these data is necessary.The annotation on metadata,the resource data which contain a meta-model of the database,is the basis of the annotating work if a semi-automated or an automated annotating approach which aims at making the database more accessible is expected.In this study,a method has been proposed to transform the terms which cannot be matched directly by changing them literally but maintaining their semantics,and then annotating them indirectly.After the transforming work,a refinement method which is reducible to phrase sense disambiguation(PSD)is employed to ensure accuracy.A pilot study on a hospital database has been conducted to test the accuracy and effectiveness of the proposed method. | Boyi Xu Ke Xu LiuLiu Fu Ling Li Weiwei Xin Hongming Cai | 2016 | Journal of Management Analytics2016，3，2： | 6 |
| 13 | Location Prediction on Trajectory Data: A Review显示文摘Location prediction is the key technique in many location based services including route navigation, dining location recommendations, and traffic planning and control, to mention a few. This survey provides a comprehensive overview of location prediction, including basic definitions and concepts, algorithms, and applications. First, we introduce the types of trajectory data and related basic concepts. Then, we review existing location-prediction methods, ranging from temporal-pattern-based prediction to spatiotemporal-pattern-based prediction. We also discuss and analyze the advantages and disadvantages of these algorithms and briefly summarize current applications of location prediction in diverse fields. Finally, we identify the potential challenges and future research directions in location prediction. | Ruizhi Wu Guangchun Luo Junming Shao Ling Tian Chengzong Peng | 2018 | Big Data Mining and Analytics2018，1，2： | 5 |
| 14 | A Novel Clustering Technique for Efficient Clustering of Big Data in Hadoop Ecosystem显示文摘Big data analytics and data mining are techniques used to analyze data and to extract hidden information.Traditional approaches to analysis and extraction do not work well for big data because this data is complex and of very high volume. A major data mining technique known as data clustering groups the data into clusters and makes it easy to extract information from these clusters. However, existing clustering algorithms, such as k-means and hierarchical, are not efficient as the quality of the clusters they produce is compromised. Therefore, there is a need to design an efficient and highly scalable clustering algorithm. In this paper, we put forward a new clustering algorithm called hybrid clustering in order to overcome the disadvantages of existing clustering algorithms. We compare the new hybrid algorithm with existing algorithms on the bases of precision, recall, F-measure, execution time, and accuracy of results. From the experimental results, it is clear that the proposed hybrid clustering algorithm is more accurate, and has better precision, recall, and F-measure values. | Sunil Kumar Maninder Singh | 2019 | Big Data Mining and Analytics2019，2，4： | 5 |
| 15 | Big data analytics and business analytics显示文摘Over the past few decades,with the development of automatic identification,data capture and storage technologies,people generate data much faster and collect data much bigger than ever before in business,science,engineering,education and other areas.Big data has emerged as an important area of study for both practitioners and researchers.It has huge impacts on data-related problems.In this paper,we identify the key issues related to big data analytics and then investigate its applications specifically related to business problems. | Lian Duan Ye Xiong | 2015 | Journal of Management Analytics2015，2，1： | 5 |
| 16 | Multi-item EPQ model with learning effect on imperfect production over fuzzy-random planning horizon显示文摘Uncertainty is certain in the world of uncertainty.This study revisits an economic production quantity(EPQ)model with shortages for stock-dependent demand of the items with reworking and disposing of the imperfect ones over a random planning horizon under the joint effect of inflation and time value of money,where the expected time length is imprecise in nature.Transmission of learning effect has been incorporated to reduce the defective production.The total expected profit over the random planning horizon is maximized subject to the imprecise space constraint.The possibility,necessity and credibility measures have been introduced to defuzzify the model.The simulation-based genetic algorithm is used to make decision for the above EPQ model in different measures of uncertainty.The model is illustrated through an example.Sensitivity analysis shows the impacts of different parameters on the objective function in the model. | Amalesh Kumar Manna Barun Das Jayanta Kumar Dey Shyamal Kumar Mondal | 2017 | Journal of Management Analytics2017，4，1： | 5 |
| 17 | Big data analytics with applications显示文摘In this paper,recent developments on the Internet of Things(IoT)and its applications are surveyed,and the impact of newly developed Big Data(BD)on manufacturing information systems is especially discussed.Big Data analytics(BDA)has been identified as a critical technology to support data acquisition,storage,and analytics in data management systems in modern manufacturing.The purpose of the presented work is to clarify the requirements of predictive systems,and to identify research challenges and opportunities on BDA to support cloudbased information systems. | Zhuming Bi David Cochran | 2014 | Journal of Management Analytics2014，1，4： | 5 |
| 18 | An inventory model with finite replenishment,probabilistic deterioration and permissible delay in payments显示文摘This paper develops an inventory model for deteriorating items with finite replenishment rate under a progressive payment scheme within the cycle time.In this model,the deterioration function follows a probability distribution such as a(1)uniform distribution,(2)triangular distribution or(3)beta distribution.Here,the retailer is allowed a trade-credit offer by the supplier to buy more items.This model aids in minimizing the total inventory cost of the retailer by finding the optimal cycle length,the optimal time length of replenishment and the optimal order quantity.Some theorems have been framed to characterize the optimal solutions.The necessary and sufficient conditions of the existence and uniqueness of the optimal solutions are also provided.The optimal solution of the model is illustrated with the help of numerical examples,and numerical comparisons between the three models are also given.Finally,sensitivity analysis and graphical representations are given to demonstrate the model. | M.Palanivel S.Priyan R.Uthayakumar | 2015 | Journal of Management Analytics2015，2，3： | 5 |
| 19 | An Improved Hybrid Collaborative Filtering Algorithm Based on Tags and Time Factor显示文摘The Collaborative Filtering(CF) recommendation algorithm, one of the most popular algorithms in Recommendation Systems(RS), mainly includes memory-based and model-based methods. When performing rating prediction using a memory-based method, the approach used to measure the similarity between users or items can significantly influence the recommendation performance. Traditional CFs suffer from data sparsity when making recommendations based on a rating matrix, and cannot effectively capture changes in user interest. In this paper, we propose an improved hybrid collaborative filtering algorithm based on tags and a time factor(TTHybridCF), which fully utilizes tag information that characterizes users and items. This algorithm utilizes both tag and rating information to calculate the similarity between users or items. In addition, we introduce a time weighting factor to measure user interest, which changes over time. Our experimental results show that our method alleviates the sparsity problem and demonstrates promising prediction accuracy. | Chunxia Zhang Ming Yang Jing Lv Wanqi Yang | 2018 | Big Data Mining and Analytics2018，1，2： | 4 |
| 20 | A comprehensive review from sequential association computing to Hadoop-MapReduce parallel computing in a retail scenario显示文摘Today,the customer’s requirements are entirely transformed.Many big retail organizations are facing sudden decline in the sales and revenues caused due to indecisive and erratic purchasing habits of recent generation of users,as they get abundant preferred information such as cheaper rates,amazing offers,discounts,comparison of similar products,etc.over their smartphones or laptops hence they straightaway place order instead of walking down to showroom.As a result,large companies such as Tesco,Wal-Mart,Target,etc.have realized that it is requisite to shake hands with startup firms which already supports platform to retain customers either via deep exploration of transactional data or by offering lucrative offers in the benefit of customer and to promote market basket.The data which are generated from consumer purchase pattern,Big Data is a concern for companies as a result various big retail organizations are applying advanced and scalable data mining algorithms to precisely store and evaluate data in real-time manner to boost market basket analysis.This research work discusses various improved association rule mining(ARM)algorithms.The objective of this study is to identify gaps,providing opportunities for new research,to recognize expansion of Big Data analytics with retail environment and its future directions.This paper assimilates various aspects of parallel ARM algorithm for market basket analysis against sequential and distributed nature which are further escalated to Hadoop and MapReduce computing platform.Further various use cases highlighting the need of‘Big Data Retail Analytics’are discussed for emerging trends to promote sales and revenues,to keep check on competitor’s websites,comparison of various brands,enticing new customers. | Neha Verma Jatinder Singh | 2017 | Journal of Management Analytics2017，4，4： | 4 |
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