Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
An overview of workflow management: From process modeling to workflow automation infrastructure
1995894 citationsDimitrios Georgakopoulos, Mark F. Hornick et al.Distributed and Parallel Databasesprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Mark F. Hornick
Since
Specialization
Citations
This map shows the geographic impact of Mark F. Hornick's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mark F. Hornick with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mark F. Hornick more than expected).
This network shows the impact of papers produced by Mark F. Hornick. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mark F. Hornick. The network helps show where Mark F. Hornick may publish in the future.
Co-authorship network of co-authors of Mark F. Hornick
This figure shows the co-authorship network connecting the top 25 collaborators of Mark F. Hornick.
A scholar is included among the top collaborators of Mark F. Hornick based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Mark F. Hornick. Mark F. Hornick is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Hornick, Mark F., et al.. (2013). Using R to Unlock the Value of Big Data: Big Data Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop. CERN Document Server (European Organization for Nuclear Research).6 indexed citations
3.
Hornick, Mark F., et al.. (2013). Oracle Big Data Handbook.10 indexed citations
Anand, Sarabjot Singh, Marko Grobelnik, Frank Herrmann, et al.. (2007). Knowledge discovery standards. Artificial Intelligence Review. 27(1). 21–56.7 indexed citations
6.
Hornick, Mark F., et al.. (2006). Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for Architecture, Design, and Implementation. CERN Document Server (European Organization for Nuclear Research).21 indexed citations
7.
Tamayo, Pablo, Charles E.H. Berger, Boriana L. Milenova, et al.. (2005). Oracle Data Mining - Data Mining in the Database Environment.. 1315–1329.3 indexed citations
8.
Grossman, Robert L., et al.. (2002). Data mining standards initiatives. Communications of the ACM. 45(8). 59–61.46 indexed citations
Georgakopoulos, Dimitrios, Mark F. Hornick, & Amit Sheth. (1995). An overview of workflow management: From process modeling to workflow automation infrastructure. Distributed and Parallel Databases. 3(2). 119–153.894 indexed citations breakdown →
Buchmann, Alejandro, M. TAMER ÖZSU, Mark F. Hornick, Dimitrios Georgakopoulos, & Frank Manola. (1992). A transaction model for active distributed object systems. Morgan Kaufmann Publishers Inc. eBooks. 123–158.47 indexed citations
16.
Manola, Frank, Sandra Heiler, Dimitrios Georgakopoulos, Mark F. Hornick, & Michael L. Brodie. (1992). DISTRIBUTED OBJECT MANAGEMENT. International Journal of Cooperative Information Systems. 1(1). 5–42.90 indexed citations
17.
Hornick, Mark F., et al.. (1991). Integrating Heterogeneous, Autonomous, Distributed Applications Using the DOM Prototype..1 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.