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Marco Mazzucco

dblp:70/4009 · DBLP profile ↗
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8ranked-venue papers
1as first author
0since 2021 · last 2006
—ORCID · none

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

Systems, architecture and hardware · 6 · 1 first-authorTheory of computation · 2

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.

Theoretical computer science
1 paper
Logic in computer science · 67% Combinatorics and discrete mathematics · 33%
Databases, data mining, and information retrieval
1 paper
Data stream processing · 77% Data mining · 23%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Interconnection networks and networks-on-chip · 100%

Topics — the 4 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Combinatorics and discrete mathematics › analytic combinatorics
limit laws
0.112006
Determined theories and limit laws · Inf. Comput. 2006
Logic in computer science
model theory
0.112006
Determined theories and limit laws · Inf. Comput. 2006
Logic in computer science › finite model theory
zero-one laws
0.112006
Determined theories and limit laws · Inf. Comput. 2006
Interconnection networks and networks-on-chip
high-speed networks
0.012002
Merging multiple data streams on common keys over high performance networks · SC 2002

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

probability · 0.1model theory · 0.1
YearPublicationVenuePosition
2006 Determined theories and limit laws
John T. Baldwin 0001, Marco Mazzucco
Inf. Comput.2
2005 Simple Available Bandwidth Utilization Library for High-Speed Wide Area Networks
Robert L. Grossman, Marco Mazzucco, Harimath Sivakumar, Y. Pan
J. Supercomput.2
2003 Experimental studies using photonic data services at IGrid 2002
Robert L. Grossman, Yunhong Gu, Don Hamelburg, David Hanley, Xinwei Hong, Jorge Levera, David J. Lillethun, Marco Mazzucco, Joe Mambretti, Jeremy Weinberger
Future Gener. Comput. Syst.8
2003 The Photonic TeraStream: enabling next generation applications through intelligent optical networking at iGRID2002
Joe Mambretti, Jeremy Weinberger, Jim Hao Chen, Elizabeth Bacon, Fei Yeh, David J. Lillethun, Robert L. Grossman, Yunhong Gu, Marco Mazzucco
Future Gener. Comput. Syst.9
2003 TeraScope: distributed visual data mining of terascale data sets over photonic networks
Jason Leigh, Thomas A. DeFanti, Marco Mazzucco, Robert L. Grossman
Future Gener. Comput. Syst.4
2003 Data webs for earth science data
Asvin Ananthanarayan, Rajiv Balachandran, Robert L. Grossman, Yunhong Gu, Xinwei Hong, Jorge Levera, Marco Mazzucco
Parallel Comput.7
2002 Merging multiple data streams on common keys over high performance networks
abstract
The model for data mining on streaming data assumes that there is a buffer of fixed length and a data stream of infinite length and the challenge is to extract patterns, changes, anomalies, and statistically significant structures by examining the data one time and storing records and derived attributes of length less than N. As data grids, data webs, and semantic webs become more common, mining distributed streaming data will become more and more important. The first step when presented with two or more distributed streams is to merge them using a common key. In this paper, we present two algorithms for merging streaming data using a common key. We also present experimental studies showing these algorithms scale in practice to OC-12 networks.
Marco Mazzucco, Asvin Ananthanarayan, Robert L. Grossman, Jorge Levera, Gokulnath Bhagavantha Rao
SC1
1999 Classification of delta-Invariant Amalgamation Classes
abstract
Hrushovski's generalization of the Fraisse construction has provided a rich source of examples in model theory, model theoretic algebra and random graph theory. The construction assigns to a dimension function δ and a class K of finite (finitely generated) models a countable ‘generic’ structure. We investigate here some of the simplest possible cases of this construction. The class K will be a class of finite graphs; the dimension, δ(A), of a finite graph A will be the cardinality of A minus the number of edges of A. Finally and significantly we restrict to classes which are δ-invariant. A class of finite graphs is δ-invariant if membership of a graph in the class is determined (as specified below) by the dimension and cardinality of the graph, and dimension and cardinality of all its subgraphs. Note that a generic graph constructed as in Hrushovski's example of a new strongly minimal set does not arise from a δ-invariant class. We show there are countably many δ-invariant (strong) amalgamation classes of finite graphs which are closed under subgraph and describe the countable generic models for these classes. This analysis provides ω-stable generic graphs with an array of saturation and model completeness properties which belies the similarity of their construction. In particular, we answer a question of Baizhanov (unpublished) and Baldwin [5] and show that this construction can yield an ω-stable generic which is not saturated. Further, we exhibit some ω-stable generic graphs that are not model complete.
Roman D. Arefev, John T. Baldwin 0001, Marco Mazzucco
J. Symb. Log.3