Dima Alberg

518 total citations
11 papers, 314 citations indexed

About

Dima Alberg is a scholar working on Signal Processing, Artificial Intelligence and Information Systems. According to data from OpenAlex, Dima Alberg has authored 11 papers receiving a total of 314 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Signal Processing, 4 papers in Artificial Intelligence and 3 papers in Information Systems. Recurrent topics in Dima Alberg's work include Time Series Analysis and Forecasting (4 papers), Data Stream Mining Techniques (3 papers) and Data Mining Algorithms and Applications (3 papers). Dima Alberg is often cited by papers focused on Time Series Analysis and Forecasting (4 papers), Data Stream Mining Techniques (3 papers) and Data Mining Algorithms and Applications (3 papers). Dima Alberg collaborates with scholars based in Israel and United States. Dima Alberg's co-authors include Haim Shalit, Rami Yosef, Mark Last, Abraham Kandel, Yossi Hadad and Baruch Keren and has published in prestigious journals such as Energies, Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery and Applied Financial Economics.

In The Last Decade

Dima Alberg

10 papers receiving 282 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Dima Alberg Israel	4	173	159	83	68	52	11	314
Peter Nystrup Denmark	12	124 0.7×	128 0.8×	125 1.5×	133 2.0×	33 0.6×	25	370
Apostolos N. Refenes Greece	5	166 1.0×	138 0.9×	132 1.6×	41 0.6×	67 1.3×	11	327
Gabriel Vasconcelos Brazil	8	171 1.0×	56 0.4×	143 1.7×	68 1.0×	136 2.6×	16	337
An-Sing Chen Taiwan	5	209 1.2×	145 0.9×	270 3.3×	58 0.9×	36 0.7×	8	373
Xing Han New Zealand	10	221 1.3×	239 1.5×	78 0.9×	46 0.7×	26 0.5×	33	415
Yuping Song China	9	123 0.7×	95 0.6×	143 1.7×	77 1.1×	12 0.2×	37	294
Yingchao Zou China	12	219 1.3×	41 0.3×	177 2.1×	164 2.4×	31 0.6×	33	412
Qin Lu China	10	131 0.8×	95 0.6×	96 1.2×	81 1.2×	15 0.3×	15	334
Robert Ślepaczuk Poland	9	146 0.8×	128 0.8×	150 1.8×	46 0.7×	14 0.3×	45	268
Jae Wook Song South Korea	10	225 1.3×	127 0.8×	96 1.2×	23 0.3×	9 0.2×	36	377

Countries citing papers authored by Dima Alberg

Since Specialization

Citations

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

Fields of papers citing papers by Dima Alberg

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dima Alberg

This figure shows the co-authorship network connecting the top 25 collaborators of Dima Alberg. A scholar is included among the top collaborators of Dima Alberg 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 Dima Alberg. Dima Alberg 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:

11 of 11 papers shown

1.

Hadad, Yossi, Baruch Keren, & Dima Alberg. (2023). An Expert System for Ranking and Matching Electric Vehicles to Customer Specifications and Requirements. Energies. 16(11). 4283–4283. 1 indexed citations

2.

Alberg, Dima, et al.. (2021). An Open Source Finance System for Stocks Backtesting Trade Strategies. 12(2). 52–65. 1 indexed citations

3.

Alberg, Dima. (2019). A Business Intelligence Project-Oriented Course. RePEc: Research Papers in Economics. 10(2). 29–35.

4.

Alberg, Dima & Mark Last. (2018). Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Vietnam Journal of Computer Science. 5(3-4). 241–249. 59 indexed citations

5.

Alberg, Dima. (2015). An Interval Tree Approach to Predict Forest Fires using Meteorological Data. International Journal of Computer Applications. 132(4). 17–22. 1 indexed citations

6.

Alberg, Dima. (2015). A Spatio-Temporal Simulation Model for Incremental Clustering in Massive Moving Objects Data Set. International Journal of Database Theory and Application. 8(2). 11–24. 1 indexed citations

7.

Alberg, Dima, et al.. (2011). Knowledge discovery in data streams with regression tree methods. Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery. 2(1). 69–78. 30 indexed citations

8.

Alberg, Dima, et al.. (2009). Induction of Mean Output Prediction Trees from Continuous Temporal Meteorological Data. 10. 208–213. 2 indexed citations

9.

Alberg, Dima, Haim Shalit, & Rami Yosef. (2008). Estimating stock market volatility using asymmetric GARCH models. Applied Financial Economics. 18(15). 1201–1208. 187 indexed citations

10.

Alberg, Dima, et al.. (2008). A Spatio-temporal Simulation Model for Movement Data Generation. 25. 320–325. 2 indexed citations

11.

Last, Mark, et al.. (2007). ADMIRAL: A Data Mining Based Financial Trading System. 720–725. 30 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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