Demo Data from EDBT 2026 / reviewers in the wild / expert
Edmond Mesrobian
dblp:84/6195
· DBLP profile ↗
6ranked-venue papers
1as first author
0since 2021 · last 2001
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Databases, data management, data science and information retrieval · 5Artificial intelligence and machine learning · 2Software engineering, systems software and programming languages · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Databases, data mining, and information retrieval
2 papers |
Data mining · 100% | |
| Interdisciplinary, comprehensive, and emerging computing
1 paper |
Environmental and earth informatics · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Data mining › big data analytics › large-scale data mining
distributed data mining |
0.0 | 1 | 1996 | Scalable Exploratory Data Mining of Distributed Geoscientific Data · KDD 1996 |
Data mining
spatiotemporal data mining |
0.0 | 1 | 1995 | Fast Spatio-Temporal Data Mining of Large Geophysical Datasets · KDD 1995 |
Environmental and earth informatics › geoscience
geophysical data analysis |
0.0 | 1 | 1995 | Fast Spatio-Temporal Data Mining of Large Geophysical Datasets · KDD 1995 |
Methods — techniques the papers use, named apart from their topics
spatio-temporal data mining · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2001 | Extensible Parallel Query Processing for Exploratory Geoscientific Data Mining
Eddie C. Shek, Richard R. Muntz, Edmond Mesrobian |
Data Min. Knowl. Discov. | 3 |
| 1997 | ESMDIS: Earth System Model Data Information SystemabstractThe goal of the development of the Earth System Model Data Information System (ESMDIS) are to provide Earth scientists with: 1) an output management system of Earth System Model (ESM) to browse the metadata and retrieve a desired subset of ESM output; 2) an analysis system of ESM output and other related datasets; 3) an automated pipelining system for ESM data processing; 4) a visualization system; and 5) a Web based user interface to utilize the system. ESMDIS is based on DBMS centric approach, built upon the "BigSur" Earth science data schema, and developed using an object relational DBMS. We have built a prototype ESMDIS, and present the results of its development. Yuechen Chi, Carlos R. Mechoso, Michael Stonebraker, Keith Sklower, Richard Troy, Richard R. Muntz, Edmond Mesrobian |
SSDBM | 7 |
| 1997 | geoPOM: A Heterogeneous Geoscientific Persistent Object SystemabstractLately, a need for uniform access to and integration of data stored in specialized, non-standard repositories such as GIS or multimedia storage servers has become apparent. The authors provide an overview of a heterogeneous geoscientific persistent object manager (geoPOM) developed at the UCLA Data Mining Laboratory. GeoPOM provides users with the "illusion" of a single object-oriented spatial data store even though the data is actually stored in several different spatial data repositories, thus, allowing users to define and handle spatial data in a uniform manner The geoPOM data model is based on the ODMG-93 standard for object-oriented data models, and the Open Geodata Consortium's (OGC) standardization effort for temporal-spatial object types (OGIS). Silvia Nittel, Richard R. Muntz, Edmond Mesrobian |
SSDBM | 3 |
| 1996 | Scalable Exploratory Data Mining of Distributed Geoscientific Data
Eddie C. Shek, Richard R. Muntz, Edmond Mesrobian, Kenneth W. Ng |
KDD | 3 |
| 1995 | Fast Spatio-Temporal Data Mining of Large Geophysical Datasets
Paul E. Stolorz, Hisashi Nakamura, Edmond Mesrobian, Richard R. Muntz, Eddie C. Shek, Jose Renato Santos, J. Yi, Kenneth W. Ng, S.-Y. Chien, Carlos R. Mechoso, John D. Farrara |
KDD | 3 |
| 1992 | A Software Environment For Studying Computational Neural SystemsabstractUCLA-SFINX is a neural network simulation environment that enables users to simulate a wide variety of neural network models at various levels of abstraction. A network specification language enables users to construct arbitrary network structures. Small, structurally irregular networks can be modeled by explicitly defining each neuron and can be modeled by explicitly defining each neuron and corresponding connections. Very large networks with regular connectivity patterns can be implicitly specified using array constructs. Graphics support, based on X Windows System, is provided to visualize simulation results. Details of the simulation environment are described, and simulation examples are presented to demonstrate SFINX's capabilities.> Edmond Mesrobian, Josef Skrzypek |
IEEE Trans. Software Eng. | 1 |