›› 2016, Vol. 31 ›› Issue (4): 787-804.doi: 10.1007/s11390-016-1663-1

Special Issue: Surveys; Computer Graphics and Multimedia

• Computer Graphics and Multimedia • Previous Articles     Next Articles

A Survey of Visual Analytic Pipelines

Xu-Meng Wang(王叙萌), Tian-Ye Zhang(张天野), Yu-Xin Ma(马昱欣), Jing Xia(夏菁), and Wei Chen*(陈为), Senior Member, IEEE   

  1. State Key Laboratory of Computer Aided Design and Computer Graphics, Zhejiang University, Hangzhou 310058, China;
    Innovation Joint Research Center for Cyber-Physical-Society System, Zhejiang University, Hangzhou 310058, China
  • Received:2016-04-17 Revised:2016-05-31 Online:2016-07-05 Published:2016-07-05
  • Contact: Wei Chen E-mail:chenwei@cad.zju.edu.cn
  • About author:Xu-Meng Wang is a Ph.D. student in the State Key Laboratory of Computer Aided Design (CAD) and Computer Graphics (CG) at Zhejiang University, Hangzhou. She earned her B.S. degree in information and computing science from Zhejiang University in 2016. Her research interest is visual analytics.
  • Supported by:

    The work was supported by the National Basic Research 973 Program of China under Grant No. 2015CB352503, the Major Program of National Natural Science Foundation of China under Grant No. 61232012, the National Natural Science Foundation of China under Grant Nos. 61422211, u1536118, and u1536119, Zhejiang Provincial Natural Science Foundation of China under Grant No. LR13F020001, and Fundamental Research Funds for the Central Universities of China.

Visual analytics has been widely studied in the past decade. One key to make visual analytics practical for both research and industrial applications is the appropriate definition and implementation of the visual analytics pipeline which provides effective abstractions for designing and implementing visual analytics systems. In this paper we review the previous work on visual analytics pipelines and individual modules from multiple perspectives:data, visualization, model and knowledge. In each module we discuss various representations and descriptions of pipelines inside the module, and compare the commonalities and the differences among them.

[1] Fayyad U M, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery:An overview. In Advances in Knowledge Discovery and Data Mining, Fayyad U M, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds.), American Association for Artificial Intelligence, Menlo Park, CA, USA, 1996, pp.1-34.

[2] Keim D, Kohlhammer J, Ellis G, Mansmann F (eds.). Mastering the information age:Solving problems with visual analytics. http://www.vismaster.eu/wp-content/uploads/2010/11/title-page-to-chapter-1.pdf, June 2016.

[3] Keim D, Andrienko G, Fekete J D et al. Visual analytics:Definition, process, and challenges. In Lecture Notes in Computer Science 4950, Kerren A, Stasko J T, Fekete J D et al. (eds.), Springer Berlin Heidelberg, 2008, pp.154-175.

[4] Zhang L, Stoffel A, Behrisch M et al. Visual analytics for the big data era-A comparative review of state-of-the-art commercial systems. In Proc. IEEE Conference on Visual Analytics Science and Technology, Oct. 2012, pp.173-182.

[5] Sun G D, Wu Y C, Liang R H, Liu S X. A survey of visual analytics techniques and applications:State-of-the-art research and future challenges. Journal of Computer Science and Technology, 2013, 28(5):852-867.

[6] Moreland K. A survey of visualization pipelines. IEEE Transactions on Visualization and Computer Graphics, 2014, 19(3):367-378.

[7] Alexander E, Gleicher M. Task-driven comparison of topic models. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):320-329.

[8] Sun M, North C, Ramakrishnan N. A five-level design framework for bicluster visualizations. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1713-1722.

[9] Zhang J, E Y, Ma J et al. Visual analysis of public utility service problems in a metropolis. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1843-1852.

[10] Keim D A. Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 2002, 8(1):1-8.

[11] Walker J, Borgo R, Jones M W. TimeNotes:A study on effective chart visualization and interaction techniques for time-series data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):549-558.

[12] Lu Y, Kruger R, Thom D, Wang F, Koch S, Ertl T, Maciejewski R. Integrating predictive analytics and social media. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Nov. 2014, pp.193-202.

[13] Ferstl F, Burger K, Westermann R. Streamline variability plots for characterizing the uncertainty in vector field ensembles. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):767-776.

[14] Skraba P, Wang B, Chen G, Rosen P. Robustness-based simplification of 2D steady and unsteady vector fields. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(8):930-944.

[15] Wang Z, Ye T, Lu M et al. Visual exploration of sparse traffic trajectory data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1813-1822.

[16] Wang F, Chen W, Wu F et al. A visual reasoning approach for data-driven transport assessment on urban roads. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.103-112.

[17] Huang X, Zhao Y, Ma C et al. TrajGraph:A graph-based visual analytics approach to studying urban network centralities using taxi trajectory data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):160-169.

[18] Vrotsou K, Janetzko H, Navarra C et al. SimpliFly:A methodology for simplification and thematic enhancement of trajectories. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(1):107-121.

[19] Palomo C, Guo Z, Silva C T, Freire J. Visually exploring transportation schedules. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):170-179.

[20] Scheepens R, Hurter C, Van De Wetering H, Van Wijk J J. Visualization, selection, and analysis of traffic flows. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):379-388.

[21] Di Lorenzo G, Sbodio M, Calabrese F et al. AllAboard:Visual exploration of cellphone mobility data to optimise public transport. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(2):1036-1050.

[22] Wu W, Xu J, Zeng H et al. TelCoVis:Visual exploration of co-occurrence in urban human mobility based on Telco data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):935-944.

[23] Zhao J, Cao N, Wen Z et al. #FluxFlow:Visual analysis of anomalous information spreading on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1773-1782.

[24] Huang D, Tory M, Aseniero B A et al. Personal visualization and personal visual analytics. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(3):420-433.

[25] Janicke S, Focht J, Scheuermann G. Interactive visual profiling of musicians. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):200-209.

[26] Glueck M, Hamilton P, Chevalier F et al. PhenoBlocks:Phenotype comparison visualizations. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):101-110.

[27] Chen H, Zhang S, Chen W et al. Uncertainty-aware multidimensional ensemble data visualization and exploration. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(9):1072-1086.

[28] Thudt A, Baur D, Huron S, Carpendale S. Visual mementos:Reflecting memories with personal data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):369-378.

[29] Wongsuphasawat K, Moritz D, Anand A et al. Voyager:Exploratory analysis via faceted browsing of visualization recommendations. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):649-658.

[30] Lex A, Gehlenborg N, Strobelt H et al. UpSet:Visualization of intersecting sets. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1983-1992.

[31] Stahnke J, Dork M, Muller B, Thom A. Probing projections:Interaction techniques for interpreting arrangements and errors of dimensionality reductions. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):629-638.

[32] Dasgupta A, Poco J, Wei Y et al. Bridging theory with practice:An exploratory study of visualization use and design for climate model comparison. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(9):996-1014.

[33] Quinan P S, Meyer M. Visually comparing weather features in forecasts. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):389-398.

[34] Accorsi P, Lalande N, Fabregue M et al. HydroQual:Visual analysis of river water quality. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.123-132.

[35] Crnovrsanin T, Muelder C, Ma K L. A system for visual analysis of radio signal data. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.33-42.

[36] Goodwin S, Dykes J, Slingsby A, Turkay C. Visualizing multiple variables across scale and geography. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):599-608.

[37] Kurzhals K, Hlawatsch M, Heimerl F et al. Gaze stripes:Image-based visualization of eye tracking data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):1005-1014.

[38] Etemadpour R, Motta R, de Souza Paiva J G et al. Perception-based evaluation of projection methods for multidimensional data visualization. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(1):81-94.

[39] Sun M, Mi P, North C, Ramakrishnan N. BiSet:Semantic edge bundling with biclusters for sensemaking. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):310-319.

[40] Brehmer M, Ingram S, Stray J, Munzner T. Overview:The design, adoption, and analysis of a visual document mining tool for investigative journalists. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):2271-2280.

[41] Gad S, Javed W, Ghani S et al. ThemeDelta:Dynamic segmentations over temporal topic models. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(5):672-685.

[42] Fulda J, Brehmer M, Munzner T. TimeLineCurator:Interactive authoring of visual timelines from unstructured text. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):300-309.

[43] Bach B, Shi C, Heulot N et al. Time curves:Folding time to visualize patterns of temporal evolution in data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):559-568.

[44] McCurdy N, Lein J, Coles K et al. Poemage:Visualizing the sonic topology of a poem. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):439-448.

[45] Brooks M, Amershi S, Lee B et al. FeatureInsight:Visual support for error-driven feature ideation in text classification. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2015, pp.105-112.

[46] Wu Y, Liu S, Yan K et al. OpinionFlow:Visual analysis of opinion diffusion on social media. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1763-1772.

[47] Gomez S R, Guo H, Ziemkiewicz C, Laidlaw D H. An insight- and task-based methodology for evaluating spatiotemporal visual analytics. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp. 63-72.

[48] Yu B, Doraiswamy H, Chen X et al. Genotet:An interactive web-based visual exploration framework to support validation of gene regulatory networks. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1903-1912.

[49] Lenz O, Keul F, Bremm S et al. Visual analysis of patterns in multiple amino acid mutation graphs. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.93-102.

[50] Skanberg R, Vazquez P P, Guallar V, Ropinski T. Real-time molecular visualization supporting diffuse interreflections and ambient occlusion. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):718-727.

[51] Shi L, Wang C, Wen Z et al. 1.5 D egocentric dynamic network visualization. IEEE Transactions on Visualization and Computer Graphics, 2015, 21(5):624-637.

[52] Janikow C Z. Fuzzy decision trees:Issues and methods. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, 1998, 28(1):1-14.

[53] Liu M, Wang X, Huang Y. Data preprocessing in data mining. Scientific Journal of Computer Science, 2000, 27(4):54-57. (in Chinese)

[54] Friedman M, Levy A Y, Millstein T D. Navigational plans for data integration. In Proc. the 16th National Conference on Artificial Intelligence and the 11th Innovative Applications of Artificial Intelligence Conference Innovative Applications of Artificial Intelligence, July 1999, pp.67-73.

[55] Lenzerini M. Data integration:A theoretical perspective. In Proc. the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, June 2002, pp.233-246.

[56] Rahm E, Do H H. Data cleaning:Problems and current approaches. IEEE Data Eng. Bull., 2000, 23(4):3-13.

[57] Chen W, Shen Z, Tao Y. Data Visualization. Publishing House of Electronics Industry, 2013. (in Chinese)

[58] Chi E H h, Riedl J T. An operator interaction framework for visualization systems. In Proc. the IEEE Symposium on Information Visualization, Oct. 1998, pp.63-70.

[59] Chi E H. A taxonomy of visualization techniques using the data state reference model. In Proc. the IEEE Symposium on Information Visualization, Oct. 2000, pp.69-75.

[60] Card S K, Mackinlay J D, Shneiderman B. Readings in Information Visualization:Using Vision to Think. Morgan Kaufmann, 1999.

[61] Van Wijk J J. The value of visualization. In Proc. the 16th IEEE Visualization Conference, Oct. 2005, pp.79-86.

[62] Munzner T. A nested model for visualization design and validation. IEEE Transactions on Visualization and Computer Graphics, 2009, 15(6):921-928.

[63] Munzner T. Visualization Analysis and Design. CRC Press, 2014.

[64] Albo Y, Lanir J, Bak P, Rafaeli S. Off the radar:Comparative evaluation of radial visualization solutions for composite indicators. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):569-578.

[65] Gschwandtner T, Bogl M, Federico P, Miksch S. Visual encodings of temporal uncertainty:A comparative user study. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):539-548.

[66] Johansson J, Forsell C. Evaluation of parallel coordinates:Overview, categorization and guidelines for future research. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):579-588.

[67] Jianu R, Rusu A, Hu Y, Taggart D. How to display group information on node-link diagrams:An evaluation. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(11):1530-1541.

[68] Lee J H, McDonnell K T, Zelenyuk A, Imre D, Mueller K. A structure-based distance metric for high-dimensional space exploration with multidimensional scaling. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(3):351-364.

[69] Kieffer S, Dwyer T, Marriott K, Wybrow M. HOLA:Human-like orthogonal network layout. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):349-358.

[70] Raidou R G, Eisemann M, Breeuwer M, Eisemann E, Vilanova A. Orientation-enhanced parallel coordinate plots. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):589-598.

[71] Lehmann D J, Theisel H. Optimal sets of projections of high-dimensional data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):609-618.

[72] Yoghourdjian V, Dwyer T, Gange G et al. High-quality ultra-compact grid layout of grouped networks. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):339-348.

[73] Wang Baldonado M Q, Woodruff A, Kuchinsky A. Guidelines for using multiple views in information visualization. In Proc. the Working Conference on Advanced Visual Interfaces, May 2000, pp.110-119.

[74] Cho I, Dou W, Wang D X, Sauda E, Ribarsky W. VAiRoma:A visual analytics system for making sense of places, times, and events in roman history. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):210-219.

[75] Roberts J C. State of the art:Coordinated & multiple views in exploratory visualization. In Proc. the 5th International Conference on Coordinated and Multiple Views in Exploratory Visualization, July 2007, pp.61-71.

[76] Papadopoulos C, Gutenko I, Kaufman A. VEEVVIE:Visual explorer for empirical visualization, VR and interaction experiments. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):111-120.

[77] Wang Y, Shen Q, Archambault D, Zhou Z, Zhu M, Yang S, Qu H. AmbiguityVis:Visualization of ambiguity in graph layouts. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):359-368.

[78] Roberts J C. Display models:Ways to classify visual representations. In Proc. IEEE Conference on Information Visualization, July 1999.

[79] Yi J S, Kang Y, Stasko J T, Jacko J A. Toward a deeper understanding of the role of interaction in information visualization. IEEE Transactions on Visualization and Computer Graphics, 2007, 13(6):1224-1231.

[80] Chuah M C, Roth S F. On the semantics of interactive visualizations. In Proc. the IEEE Symposium on Information Visualization, Oct. 1996, pp.29-36.

[81] Lam H. A framework of interaction costs in information visualization. IEEE Transactions on Visualization and Computer Graphics, 2008, 14(6):1149-1156.

[82] Witten I H, Frank E. Data Mining:Practical Machine Learning Tools and Techniques. Morgan Kaufmann, 2005.

[83] Ma Y, Cao Z, Wei C. A survey of visualization-driven interactive data mining approaches. Journal of Computer-Aided Design {& Computer Graphics}, 2016, 28(1):1-8. (in Chinese)

[84] De Oliveira M C F, Levkowitz H. From visual data exploration to visual data mining:A survey. IEEE Transactions on Visualization and Computer Graphics, 2003, 9(3):378-394.

[85] Ma K L. Machine learning to boost the next generation of visualization technology. IEEE Transactions on Computer Graphics and Applications, 2007, 27(5):6-9.

[86] Bertini E, Lalanne D. Surveying the complementary role of automatic data analysis and visualization in knowledge discovery. In Proc. the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery:Integrating Automated Analysis with Interactive Exploration, June 2009, pp.12-20.

[87] Klemm P, Lawonn K, Glaβer S et al. 3D regression heat map analysis of population study data. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):81-90.

[88] Han J, Kamber M, Pei J. Data Mining:Concepts and Techniques (3rd edition). Morgan Kaufmann, 2011.

[89] Lu J, Ma Y, Chen W et al. Recent progress and trends in predictive visual analytics. Frontiers of Computer Science, 2016. (accepted)

[90] Jain A, Zongker D. Feature selection:Evaluation, application, and small sample performance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(2):153-158.

[91] Dash M, Liu H. Feature selection for classification. Intelligent Data Analysis, 1997, 1(1/2/3/4):131-156.

[92] Dy J G, Brodley C E. Feature selection for unsupervised learning. The Journal of Machine Learning Research, 2004, 5:845-889.

[93] Seo J, Shneiderman B. A rank-by-feature framework for interactive exploration of multidimensional data. Information Visualization, 2005, 4(2):96-113.

[94] Krause J, Perer A, Bertini E. INFUSE:Interactive feature selection for predictive modeling of high dimensional data. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1614-1623.

[95] Markovitch S, Rosenstein D. Feature generation using general constructor functions. Machine Learning, 2002, 49(1):59-98.

[96] Schuller B, Reiter S, Rigoll G. Evolutionary feature generation in speech emotion recognition. In Proc. the IEEE International Conference on Multimedia and Expo, July 2006, pp.5-8.

[97] Zahalka J, Worring M. Towards interactive, intelligent, and integrated multimedia analytics. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.3-12.

[98] Janetzko H, Sacha D, Stein M et al. Feature-driven visual analytics of soccer data. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.13-22.

[99] Zhao J, Gou L, Wang F, Zhou M. Pearl:An interactive visual analytic tool for understanding personal emotion style derived from social media. In Proc. the IEEE Conference on Visual Analytics Science and Technology, Oct. 2014, pp.203-212.

[100] Kay M, Heer J. Beyond Weber's law:A second look at ranking visualizations of correlation. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):469-478.

[101] Harrison L, Yang F, Franconeri S, Chang R. Ranking visualizations of correlation using Weber's law. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1943-1952.

[102] Bogl M, Aigner W, Filzmoser P et al. Visual analytics for model selection in time series analysis. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12):2237-2246.

[103] Ware C. Information Visualization:Perception for Design (3rd edition). Morgan Kaufmann, 2012, pp.388-391.

[104] Pirolli P, Card S. The sensemaking process and leverage points for analyst technology as identified through cognitive task analysis. In Proc. the International Conference on Intelligence Analysis, May 2005, pp.2-4.

[105] Sacha D, Stoffel A, Stoffel F et al. Knowledge generation model for visual analytics. IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12):1604-1613.

[106] Ma Y, Chen W, Ma X et al. EasySVM:A visual analysis approach for open-box support vector machines. In Proc. IEEE VIS Workshop on Visualization for Predictive Analytics, Nov. 2014.

[107] Sacha D, Senaratne H, Kwon B C, Ellis G, Keim D A. The role of uncertainty, awareness, and trust in visual analytics. IEEE Transactions on Visualization and Computer Graphics, 2016, 22(1):240-249.

[108] Green T M, Ribarsky W, Fisher B. Building and applying a human cognition model for visual analytics. Information visualization, 2009, 8(1):1-13.

[109] Dykes J, MacEachren A, Kraak M. Beyond tools:Visual support for the entire process of GIScience. In Exploring Geovisualization, Dykes J, MacEachren A M, Kraak M J (eds.), Elsevier Ltd., 2005, pp.83-99.

[110] Klein G, Moon B, Hoffman R R. Making sense of sensemaking 2:A macrocognitive model. IEEE Transactions on Intelligent Systems, 2006, 21(5):88-92.

[111] Legrenzi P, Girotto V, Johnson-Laird P N. Focussing in reasoning and decision making. Cognition, 1993, 49(1/2):37-66.

[112] Andrews C, North C. The impact of physical navigation on spatial organization for sensemaking. IEEE Transactions on Visualization and Computer Graphics, 2013, 19(12):2207-2216.

[113] Callahan S P, Freire J, Santos E et al. VisTrails:Visualization meets data management. In Proc. the 2006 ACM SIGMOD International Conference on Management of Data, June 2006, pp.745-747.
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[1] Liu Mingye; Hong Enyu;. Some Covering Problems and Their Solutions in Automatic Logic Synthesis Systems[J]. , 1986, 1(2): 83 -92 .
[2] Chen Shihua;. On the Structure of (Weak) Inverses of an (Weakly) Invertible Finite Automaton[J]. , 1986, 1(3): 92 -100 .
[3] Gao Qingshi; Zhang Xiang; Yang Shufan; Chen Shuqing;. Vector Computer 757[J]. , 1986, 1(3): 1 -14 .
[4] Chen Zhaoxiong; Gao Qingshi;. A Substitution Based Model for the Implementation of PROLOG——The Design and Implementation of LPROLOG[J]. , 1986, 1(4): 17 -26 .
[5] Huang Heyan;. A Parallel Implementation Model of HPARLOG[J]. , 1986, 1(4): 27 -38 .
[6] Min Yinghua; Han Zhide;. A Built-in Test Pattern Generator[J]. , 1986, 1(4): 62 -74 .
[7] Tang Tonggao; Zhao Zhaokeng;. Stack Method in Program Semantics[J]. , 1987, 2(1): 51 -63 .
[8] Min Yinghua;. Easy Test Generation PLAs[J]. , 1987, 2(1): 72 -80 .
[9] Sun Yongqiang; Lu Ruzhan; Huang Xiaorong;. Termination Preserving Problem in the Transformation of Applicative Programs[J]. , 1987, 2(3): 191 -201 .
[10] Zhu Hong;. Some Mathematical Properties of the Functional Programming Language FP[J]. , 1987, 2(3): 202 -216 .

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