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Frontiers of Engineering Management

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Front. Eng    2020, Vol. 7 Issue (1) : 27-46    https://doi.org/10.1007/s42524-020-0092-6
REVIEW ARTICLE
A review of systematic evaluation and improvement in the big data environment
Feng YANG(), Manman WANG
School of Management, University of Science and Technology of China, Hefei 230026, China
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Abstract

The era of big data brings unprecedented opportunities and challenges to management research. As one of the important functions of management decision-making, evaluation has been given more functions and application space. Exploring the applicable evaluation methods in the big data environment has become an important subject of research. The purpose of this paper is to provide an overview and discussion of systematic evaluation and improvement in the big data environment. We first review the evaluation methods based on the main analytic techniques of big data such as data mining, statistical methods, optimization and simulation, and deep learning. Focused on the characteristics of big data (association feature, data loss, data noise, and visualization), the relevant evaluation methods are given. Furthermore, we explore the systematic improvement studies and application fields. Finally, we analyze the new application areas of evaluation methods and give the future directions of evaluation method research in a big data environment from six aspects. We hope our research could provide meaningful insights for subsequent research.
Keywords big data      evaluation methods      systematic improvement      big data analytic techniques      data mining     
Corresponding Author(s): Feng YANG   
Just Accepted Date: 10 January 2020   Online First Date: 21 February 2020    Issue Date: 02 March 2020
 Cite this article:   
Feng YANG,Manman WANG. A review of systematic evaluation and improvement in the big data environment[J]. Front. Eng, 2020, 7(1): 27-46.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-020-0092-6
https://academic.hep.com.cn/fem/EN/Y2020/V7/I1/27
Papers Data mining techniques Evaluation methods Evaluation indexes Main findings
Bai et al. (2015) Association rule
Classification		 Assortment efficiency evaluation Sales efficiency
Product variety		 They present a model for assortment planning and optimization for multiple stores of a company.
Liu et al. (2016b) Cloud computing
Text mining		 A structured analysis of unstructured data Forecasting accuracy The information content of Tweets and their timeliness significantly improve forecasting accuracy.
Kopcso and Pachamanova (2018) Classification
Simulation		 Predictive and prescriptive analytics Business value They evaluate the level of an organization’s maturity by a predictive and prescriptive analytics model.
Jagabathula et al. (2018) Clustering A method-based embedding technique Diverse preference observations The proposed method outperforms standard latent class, empirical Bayesian, and demographic-based techniques.
Das et al. (2016) Text mining Quantitative attributes Corporate performance They track some sentiment over time and use it to model various qualitative factors.
Lutu and Engelbrecht (2013) Aggregation
Classification		 pVn classification Predictive performance Positive-versus-negative classification is a new method for the implementation of base models for aggregation.
Roy et al. (2019) Classification An evaluation method for platforms Accuracy area under the curve F-score They present a method that compares the performances of different machine learning platforms.
Sato et al. (2019) Clustering
Association rule		 A data mining technique Knowledge discovery This study provides a useful tool for effective design solutions.
Tab.1  Evaluation methods based on data mining
Papers Statistics techniques Evaluation methods Evaluation objects or indexes Main findings
Sagaert et al. (2018) Regression A forecasting method The accuracy of sales predictions The method can automate the identification of key leading indicators from a massive data set.
Jiang et al. (2019) Simulation
Regression		 A logistic regression model Portfolio risks Their model can predict portfolio risk measures and evaluate classify risk levels at any time.
Chehrazi and Weber (2015) Maximum-likelihood estimation Dynamic collectability score (DCS) The delinquent probability of credit-card accounts This paper constructs a DCS which can be used to estimate the probability of delinquent credit-card accounts.
Ansari et al. (2018) Bayes statistics Stochastic variational Bayesian (SVB) Numerical ratings
Product attributes
Texts		 Their method can achieve fast, scalable, and accurate estimation.
Shang et al. (2017) Bayesian non-parametric The probit stick-breaking process (PSBP) mixture model Transport risk The method can generate baseline airline performance to supplier evaluation and separate recurrent risks from disruption risks.
Salemi et al. (2019) Gaussian Markov random fields Single- and multi-resolution algorithms Ranking and selection in all solutions These algorithms can self-terminate well short of infinite effort with statistical assurance about the optimality gap.
DeFond et al. (2017) Statistical analysis Propensity score matching (PSM) Audit quality The majority of PSM design choices support a Big N Effect for most of the audit quality measures.
Park et al. (2017) Cluster wise linear regression Mathematical programming-based approach Stock-keeping unit (SKU)-clustering problem They propose an exact mathematical programming-based approach and show more performance on SKU-clustering problem.
Yang et al. (2015) Stochastic multi-criteria Stochastic multi-attribute acceptability analysis-portfolio optimization (SMAA-PO) Project portfolio optimization The method provides a feasible procedure for project portfolio optimization problems.
Tab.2  Evaluation methods based on statistics
Papers Evaluation methods Evaluation objects or indexes Main findings
Hoeksma and Uetz (2016) A convex decomposition algorithm Total expected payments They study the design of mechanisms for a sequencing problem.
Bertsimas et al. (2019a) A method that exploits origin-destination data The travel time estimation Their algorithm can provide insights about urban traffic patterns on different scales and accurate travel time estimations.
Bertsimas et al. (2019b) Routing optimization algorithm Online vehicle routing Their algorithms allow dispatching in real-time thousands of taxis serving more than 25000 customers per hour.
Hochbaum (2018) Combinatorial methods based on flow procedures Accuracy, running time, scalability The algorithms describe the classification problem as a network flow problem on a graph.
Naghdi et al. (2018) Quasi-Newton trust-region algorithm Distribution system The proposed method is accurate and valid by testing two networks.
Huang et al. (2019) Two-stage data-analytic modeling approach Customer game-play via matching The two-stage data-analytic method can serve as a template for modeling customer–firm interactions.
Ball et al. (2018) Optimized stochastic annealing (OSA) Optimal sampling OSA is an effective means to find a solution with the best-expected performance.
Yang et al. (2016b) Data envelopment analysis (DEA) Cost, risk, utility The optimal allocation strategies are obtained.
Adomavicius and Zhang (2016) Evaluation of recommender systems Classification stability, ranking stability, top-K stability The study generalizes the notion of stability to a recommendation setting and develops corresponding stability metrics.
Tab.3  Evaluation methods based on optimization and simulation
Papers Evaluation methods Evaluation objects Main findings
Xia et al. (2019) Conditional restricted Boltzmann machines Consumer shopping patterns The proposed model should find application in marketing practice, especially in online or mobile marketing.
Adamopoulos et al. (2018) Method of DeepWalk The likelihood of a subsequent purchase The level of personality similarity has a positive and significant effect on the likelihood of subsequent purchase.
Wang and Li (2019) State-of-the-art deep-learning The quality of Wikipedia They validate the effectiveness of the proposed model by extensive experiment.
Borovkova and Tsiamas (2019) LSTM neural networks Intraday stock predictions The proposed model shows better performance than the benchmark models.
Galeshchuk and Mukherjee (2017) Deep convolution neural networks Forex rates Trained deep networks can achieve satisfactory out-of-sample prediction accuracy.
Timoshenko and Hauser (2019) Deep-learning architecture Customer needs Deep-learning efficiency gains are 15%–22%.
Amorin et al. (2019) A hybrid deep learning architecture Classifying microscopic damage The accuracy of the hybrid architecture is shown to be significantly improved.
Tab.4  Evaluation methods based on deep learning
Papers Evaluation methods Evaluation objects Main findings
Aung et al. (2019) FP-Growth method Customer churn They apply the FP-Growth method to the retail company’s customer churn data set for the promotion in marketing.
Zhang et al. (2019) An improved association rule mining-based method Energy efficiency They show that the proposed approach is effective in outlier identification and data transformation.
Parkinson et al. (2016) A novel method of modeling file system permissions Auditing efficiency Their method can correctly identify irregularities with an average accuracy rate of 91%, minimizing the reliance on expert knowledge.
Bhatia (2019) An approach based on the structure of word distribution Similarity measures The proposed approach can be used to predict real-time changes in risk perception and representation.
Wang and Wu (2019) The novel
systematic algorithm paradigm		 Energy returns-ratio The system can be used as a hybrid vehicle detection and vehicle maintenance standard equipment.
Boudellioua et al. (2016) A system utilizing rule mining techniques Semantic similarity Their method has a very high accuracy of pathway identification with an F1-measure of 0.987 and AUC (area under curve) of 0.99.
Czibula et al. (2019) Concurrent relational association rule (CRAR) mining Strong scaling efficiency CRAR significantly reduces the time required.
Feng et al. (2016) Soft set-based association rule mining approach σ-M-strong
γ-M-reliable		 They present an example to illustrate potential applications of the proposed method in clinical diagnosis.
Tab.5  Evaluation methods based on data association features
Papers Evaluation methods Evaluation objects Main findings
Wu et al. (2019b) Machine learning-based imputation methods Breast cancer The method gains strong robustness and discriminant power even the data set with a high missing rate (>50%).
Li et al. (2019) Vector auto-regression approach Traffic collision The proposed VAR shows better performance than other missing value imputation techniques.
Moreau et al. (2012) A statistical approach Life cycle assessment Authors show how missing data of material and energy flows to evaluate the hydropower plants.
Jia and Wu (2019) Monte Carlo simulation Five methods RFIML and MI-LV combined with cat-DWLS seemed the best methods.
Lou et al. (2019) A principal stratification causal framework Clinical endpoint bioequivalence Their work is the first time causal inference has been applied in the assessment of equivalence.
Dudel and Klüsener (2018) Compare two imputation approaches Men’s fertility They encourage data providers to take a conditional approach or provide raw data by age of mother and child.
Yang et al. (2020) An approach based on decision tree Power system The method can achieve data identification and recovery efficiently.
Tab.6  Evaluation methods considering data loss
Papers Evaluation methods Evaluation objects Main findings
Rezghi and Obulkasim (2014) Noise-free PCA (NFPCA) Two types of cancers NFPCA produces highly informative with a lower computational cost.
Zoph et al. (2016) A transfer learning method The performance of machine translation The mode can improve the syntax-based machine translation by an average of 1.3 BLEU.
van Vliet and Salmelin (2020) Post-hoc modification of linear models Electroencephalography reading data The decoding accuracy can be boosted by incorporating information about.
Wiwatcharakoses and Berrar (2019) Self-organizing incremental neural networks (SOINN) Synthetic and real-world data sets SOINN can reveal clusters of arbitrary shapes in streams of noisy data.
Ball et al. (2018) Two-dimensional mapping approach Traveling salesperson problem The proposed approach is indeed more efficient than several previously proposed simulated annealing variants.
Vanli et al. (2013) A Bayesian approach to robust parameter design Injection molding process The method can achieve more effective process control.
Huang et al. (2017) Bayesian hierarchical models Alzheimer’s disease They demonstrate the superiority of the proposed transfer learning approach.
Tab.7  Evaluation methods considering data noise
Papers Evaluation methods Evaluation objects Main findings
France and Ghose (2016) A statistical likelihood Market structure Their method is better at identifying market structure than other methods described.
Ringel and Skiera (2016) Two-dimensional mapping approach Multidimensional scaling Their method outperforms traditional models.
Nie et al. (2019a) A multimodal quantitative method Cultivars of herbal medicines They propose a comprehensive method to identify the quality of herbal medicines.
Nie et al. (2019b) Principal component analysis (PCA) Majiayou pomelo (MP) The optimal harvesting period of MP for each year is determined to be early November by PCA.
Rajwan et al. (2013) Visualizing central line Associated blood stream infection A recommended format for visualizing Central Line Associated Bloodstream Infections (CLABSI) outcome data is summarized.
Tab.8  Evaluation methods based on visualization
Papers Data processing techniques Systems Main findings
Lim and Maglio (2018) Data mining
Machine learning		 Smart service system They aggregate the key concepts of smart service systems based on big text data.
Ghose et al. (2012) Data mining Ranking systems They validate the superiority of the ranking system by several thousand users’ data sets.
Liu et al. (2016b) Data mining Online platform Information content and timeliness can significantly improve forecasting accuracy.
Distelhorst et al. (2017) Statistics Production system Lean manufacturing and high involvement work practices can improve social performance.
Ramasubbu and Kemerer (2016) Statistics Enterprise software system They illustrate how firms evaluate the business risk due to technical debt accumulation.
Bai et al. (2012) Statistics Accounting information system They propose a method to manage the risk of the quality of data.
Naghdi et al. (2018) Optimization Distribution system The proposed method is accurate and valid by testing two networks.
Ansari et al. (2018) Optimization Internet recommender system A new heterogeneous supervised model can generate much better predictions.
Buijs et al. (2016) Simulation Transport system They propose an evaluation method to improve the collaborative transport planning process.
Sun and Vasarhelyi (2018) Deep learning Risk system Deep neural network has a higher F score and better overall predictive performance.
Chung et al. (2017) Deep learning Flight system Their method can improve the accuracy of flight delay prediction.
Tab.9  Systematic improvement research based on big data processing
Papers Information features Improved methods Systems Main findings
Li and Gu (2019) Information sharing Integration approach Software system Their approach can effectively reduce the development complexity and improve the development efficiency.
Kishore et al. (2020) Information sharing Matched odds ratios (MORs) Surveillance system Their methods carry out a similar evaluation of domestic airports and the system efficiency is improved significantly.
Buckman et al. (2019) Information disclosure Experimental methods Information system Participants’ privacy valuations are largely unaffected by requiring personal disclosure.
Choi et al. (2017a) Information privacy Latent Dirichlet allocation Scopus database Algorithms, Facebook privacy, and online social networks have become prominent topics.
Tab.10  Systematic improvement methods based on information features
Papers Evaluation methods Topics Main findings
Wu et al. (2019a) Data analytics Supply chain Data analytics capabilities are most strongly associated with innovation.
Yang et al. (2017a) Game theory Supply chain The retailer shares more cost but less capacity quantity in the partial capacity cost sharing contract (PCCSC) than that in the full capacity cost sharing contract (FCCSC).
Newman et al. (2014) Parameter estimation routine Revenue management Their method is efficient and can easily incorporate price and other product attributes.
Badiezadeh et al. (2018) Data envelopment analysis (DEA) Sustainability The novel method ranks the sustainability scores of supply chains.
Huang and van Mieghem (2014) Statistical approach Inventory management A new decision support model can reduce 3% holding cost and 5% back-ordering cost.
Bertsimas et al. (2016) Stochastic optimization Inventory management They utilize conditional stochastic optimization to obtain the optimal inventory management scheme for the retail network.
Cui et al. (2018) Feature extractions
Statistical machine learning		 Sales forecast They show how the quantity and quality of user-generated data enhance product forecasts.
Boone et al. (2018) Time series models Sales forecast Their model improves sales forecast accuracy for multiple products for online retailers.
Jamshidi et al. (2017) An evaluation method for rail surface defects Risk assessment The railway fault assessment system they proposed performs well.
Chan et al. (2018) A fuzzy evaluation Risk assessment They demonstrate the practicality of the risk evaluation model.
Tab.11  Supply chain management and operations
Fig.1  Application summary of systematic evaluation and future application field.
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