Johnson Apacible

1.3k total citations · 1 hit paper
8 papers, 677 citations indexed

About

Johnson Apacible is a scholar working on Information Systems, Computer Vision and Pattern Recognition and Information Systems and Management. According to data from OpenAlex, Johnson Apacible has authored 8 papers receiving a total of 677 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Information Systems, 4 papers in Computer Vision and Pattern Recognition and 4 papers in Information Systems and Management. Recurrent topics in Johnson Apacible's work include Personal Information Management and User Behavior (4 papers), User Authentication and Security Systems (3 papers) and Data Visualization and Analytics (3 papers). Johnson Apacible is often cited by papers focused on Personal Information Management and User Behavior (4 papers), User Authentication and Security Systems (3 papers) and Data Visualization and Analytics (3 papers). Johnson Apacible collaborates with scholars based in United States, United Kingdom and Canada. Johnson Apacible's co-authors include Eric Horvitz, Trishul Chilimbi, Karthik Kalyanaraman, Paul Koch, Michael Gamon, Tae Yano, Xinying Song, Patrick Pantel, Shahab Kamali and JJ Cadiz and has published in prestigious journals such as Operating Systems Design and Implementation and Journal of Convergence Information Technology.

In The Last Decade

Johnson Apacible

7 papers receiving 622 citations

Hit Papers

align trajectories

What are hit papers?

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.

Project Adam: building an efficient and scalable deep learning training system

2014 352 citations Trishul Chilimbi, Johnson Apacible et al. Operating Systems Design and Implementation profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Johnson Apacible United States	5	292	289	225	145	123	8	677
Andrew Faulring United States	13	170 0.6×	169 0.6×	139 0.6×	296 2.0×	86 0.7×	21	672
Paul Calder Australia	12	169 0.6×	164 0.6×	66 0.3×	185 1.3×	117 1.0×	38	680
Daniel C. Halbert Germany	5	372 1.3×	152 0.5×	61 0.3×	290 2.0×	128 1.0×	7	866
Patrick Chiu United States	19	164 0.6×	578 2.0×	122 0.5×	91 0.6×	44 0.4×	58	1.0k
Quentin Stafford-Fraser United Kingdom	5	65 0.2×	226 0.8×	167 0.7×	198 1.4×	292 2.4×	10	824
JJ Cadiz United States	8	128 0.4×	200 0.7×	313 1.4×	197 1.4×	39 0.3×	11	610
Michael Dixon Australia	11	66 0.2×	166 0.6×	50 0.2×	129 0.9×	237 1.9×	51	558
Paul Vickers United Kingdom	13	99 0.3×	242 0.8×	39 0.2×	163 1.1×	36 0.3×	57	556
Mirko Sužnjević Croatia	19	126 0.4×	431 1.5×	24 0.1×	95 0.7×	355 2.9×	60	812
Raman Sarin United States	13	149 0.5×	255 0.9×	464 2.1×	352 2.4×	59 0.5×	14	914

Countries citing papers authored by Johnson Apacible

Since Specialization

Citations

This map shows the geographic impact of Johnson Apacible'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 Johnson Apacible with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Johnson Apacible more than expected).

Fields of papers citing papers by Johnson Apacible

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Johnson Apacible. 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 Johnson Apacible. The network helps show where Johnson Apacible may publish in the future.

Co-authorship network of co-authors of Johnson Apacible

This figure shows the co-authorship network connecting the top 25 collaborators of Johnson Apacible. A scholar is included among the top collaborators of Johnson Apacible 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 Johnson Apacible. Johnson Apacible is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Chilimbi, Trishul, et al.. (2014). Project Adam: building an efficient and scalable deep learning training system. Operating Systems Design and Implementation. 571–582. 352 indexed citations breakdown →

2.

Gamon, Michael, Tae Yano, Xinying Song, Johnson Apacible, & Patrick Pantel. (2013). Identifying salient entities in web pages. 2375–2380. 23 indexed citations

3.

Song, Xinying & Johnson Apacible. (2013). Fast Tree Matching for Web Mining. Journal of Convergence Information Technology. 8(8). 1–9.

4.

Kamali, Shahab, et al.. (2012). Answering math queries with search engines. 43–52. 4 indexed citations

5.

Horvitz, Eric, Paul Koch, & Johnson Apacible. (2004). BusyBody. 507–510. 109 indexed citations

6.

Horvitz, Eric, Johnson Apacible, Raman Sarin, et al.. (2003). Experiences with the Design, Fielding, and Evaluation of a Real-Time Communications Agent. 2 indexed citations

7.

Horvitz, Eric & Johnson Apacible. (2003). Learning and reasoning about interruption. 20–27. 178 indexed citations

8.

Horvitz, Eric & Johnson Apacible. (2003). Learning and reasoning about interruption. 20–20. 9 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.

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