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Ernest Szeto

dblp:25/1416 · DBLP profile ↗
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7ranked-venue papers
0as first author
0since 2021 · last 2014
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Databases, data management, data science and information retrieval · 6Applied, interdisciplinary, general and emerging computing · 1

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.

Interdisciplinary, comprehensive, and emerging computing
3 papers
Bioinformatics and computational biology · 100%
Databases, data mining, and information retrieval
2 papers
Data models and query languages · 34% Database system architecture and tuning · 34% Data integration and cleaning · 31%

Topics — the 5 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › bioinformatics infrastructure
biological data management
0.112005
The Integrated Microbial Genomes (IMG) System: A Case Study in Biological Data Management · VLDB 2005
Bioinformatics and computational biology › biological database
biological database querying
0.011998
Advanced Query Mechanisms for Biological Databases · ISMB 1998
Data models and query languages
object-oriented data model
0.011997
Developing and Accessing Scientific Databases with the OPM Data Management Tools · ICDE 1997
Data integration and cleaning › scientific data integration
biological data integration
0.012005
The Integrated Microbial Genomes (IMG) System: A Case Study in Biological Data Management · VLDB 2005
Bioinformatics and computational biology › biological database
molecular biology databases
0.011997
Developing and Accessing Scientific Databases with the OPM Data Management Tools · ICDE 1997

Methods — techniques the papers use, named apart from their topics

schema translation · 0.0database retrofitting · 0.0
YearPublicationVenuePosition
2014 Maintaining a microbial genome & metagenome data analysis system in an academic setting
abstract
The Integrated Microbial Genomes (IMG) system integrates microbial community aggregate genomes (metagenomes) with genomes from all domains of life. IMG provides tools for analyzing and reviewing the structural and functional annotations of metagenomes and genomes in a comparative context. At the core of the IMG system is a data warehouse that contains genome and metagenome datasets provided by scientific users, as well as public bacterial, archaeal, eukaryotic, and viral genomes from the US National Center for Biotechnology Information genomic archive and a rich set of engineered, environmental and host associated metagenomes. Genomes and metagenome datasets are processed using IMG's microbial genome and metagenome sequence data processing pipelines and then are integrated into the data warehouse using IMG's data integration toolkit. Microbial genome and metagenome application specific user interfaces provide access to different subsets of IMG's data and analysis toolkits. Genome and metagenome analysis is a gene centric iterative process that involves a sequence (composition) of data exploration and comparative analysis operations, with individual operations expected to have rapid response time.
I-Min A. Chen, Victor M. Markowitz, Ernest Szeto, Krishna Palaniappan, Ken Chu
SSDBM3
2005 The Integrated Microbial Genomes (IMG) System: A Case Study in Biological Data Management
Victor M. Markowitz, Frank Korzeniewski, Krishna Palaniappan, Ernest Szeto, Natalia Ivanova, Nikos Kyrpides
VLDB4
1998 Exploring Heterogeneous Biological Databases: Tools and Applications
Anthony Kosky, I-Min A. Chen, Victor M. Markowitz, Ernest Szeto
EDBT4
1998 Advanced Query Mechanisms for Biological Databases
I-Min A. Chen, Anthony Kosky, Victor M. Markowitz, Ernest Szeto, Thodoros Topaloglou
ISMB4
1997 Developing and Accessing Scientific Databases with the OPM Data Management Tools
abstract
Summary form only given. The Object-Protocol Model (OPM) data management tools provide facilities for rapid development, documentation, and flexible exploration of scientific databases. The tools are based on OPM, an object-oriented data model which is similar to the ODMG standard, but also supports extensions for modeling scientific data. Databases designed using OPM can be implemented using a variety of commercial relational DBMSs, using schema translation tools that generate complete DBMS database definitions from OPM schemas. Further, OPM schemas can be retrofitted on top of existing databases defined using a variety of notations, such as the relational data model or the ASN.1 data exchange format, using OPM retrofitting tools. Several archival molecular biology databases have been designed and implemented using the OPM tools, including the Genome Database (GDB) and the Protein Data Bank (PDB), while other scientific databases, such as the Genome Sequence Database (GSDB), have been retrofitted with semantically enhanced views using the OPM tools.
I-Min A. Chen, Anthony Kosky, Victor M. Markowitz, Ernest Szeto
ICDE4
1997 Developing and Accessing Scientific Databases with the Object-Protocol (OPM) Data Management Tools
abstract
The Object-Protocol Model (OPM) data management tools provide facilities for rapid development, documentation, and flexible exploration of scientific databases. The tools are based on OPM, an object oriented data model which is similar to the ODMG standard, but also supports extensions for modeling scientific data (L.A. Chen and V.M. Markowitz, 1995). Databases designed using OPM can be implemented using a variety of commercial relational DBMSs, using schema translation tools that generate complete DBMS database definitions from OPM schemas (L.A. Chen and V.M. Markowitz, 1996). Further OPM schemas can be retrofitted on top of existing databases defined using a variety of notations, such as the relational data model or the ASN.1 data exchange format, using OPM retrofitting tools (L.A. Chen et al., 1997).
I-Min A. Chen, Anthony Kosky, Victor M. Markowitz, Ernest Szeto
SSDBM4
1997 Constructing and Maintaining Scientific Database Views in the Framework of the Object-Protocol Model
abstract
Scientific databases (ScDBs) are used to archive and retrieve data describing objects of scientific inquiry. Since these ScDBs must provide continuous and efficient access to large communities of scientists, they are often developed with reliable commercial relational database management systems (DBMSs) or file systems. However, relational DBMSs and flat files do not provide constructs for representing directly ScDB-specific objects and experimental procedures, and therefore they are often hard to develop, maintain, and explore. In this paper, we present a retrofitting tool for constructing and maintaining ScDB views using an object-oriented data model, and describe our experience with retrofitting ScDBs that have been originally developed using relational DBMSs and file systems. The retrofitting tool is part of a data management toolkit based on the Object-Protocol Model (OPM). The OPM toolkit provides facilities for developing databases defined using OPM and for querying and browsing...
I-Min A. Chen, Anthony Kosky, Victor M. Markowitz, Ernest Szeto
SSDBM4