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Luis Ramos

dblp:64/2503 · DBLP profile ↗
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4ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · unresolved

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

Databases, data management, data science and information retrieval · 3Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1Applied, 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.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Distributed systems · 30% Storage systems · 30% High-performance computing · 30%
Databases, data mining, and information retrieval
1 paper
Distributed and cloud data management · 100%
Computer graphics and multimedia
1 paper
Multimedia analysis and retrieval · 100%
Computer networks
1 paper
Content delivery and video streaming · 100%

Topics — the 6 heaviest of 8, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Distributed systems › replication
data replication
0.011993
An Evaluation of Alternative Virtual Replication Strategies for Continuous Retrieval of Multimedia Data · SIGMETRICS 1993
Storage systems
multimedia storage
0.011993
Continuous Retrieval of Multimedia Data Using Parallelism · IEEE Trans. Knowl. Data Eng. 1993
High-performance computing
parallel i/o
0.011993
Continuous Retrieval of Multimedia Data Using Parallelism · IEEE Trans. Knowl. Data Eng. 1993
Distributed and cloud data management › data placement
object placement
0.011991
Object Placement in Parallel Hypermedia Systems · VLDB 1991
Multimedia analysis and retrieval › multimedia retrieval
continuous media retrieval
0.011993
An Evaluation of Alternative Virtual Replication Strategies for Continuous Retrieval of Multimedia Data · SIGMETRICS 1993
Performance modeling and evaluation
simulation
0.011993
Continuous Retrieval of Multimedia Data Using Parallelism · IEEE Trans. Knowl. Data Eng. 1993

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

declustering · 0.0replication strategies · 0.0simulation · 0.0data replication · 0.0
YearPublicationVenuePosition
2017 Ontology to Profile User Models with Disabilities
Brunil Dalila Romero-Mariño, Vanesa Espín, María José Rodríguez-Fórtiz, María Visitación Hurtado, Luis Ramos, Hisham M. Haddad
MEDI5
1993 An Evaluation of Alternative Virtual Replication Strategies for Continuous Retrieval of Multimedia Data
abstract
During the past decade, information technology has evolved to store and retrieve multimedia data (e.g., audio, video). Multimedia information systems utilize a variety of human senses to provide an effective means of conveying information. Already, these systems play a major role in educational applications, entertainment technology, and library information systems. A challenging task when implementing these systems is to support a continuous retrieval of an object at the bandwidth required by its media type. This is challenging because certain media types, in particular video, require very high bandwidths. For example, the bandwidth required by NTSC (the US standard established by the National Television System Committee) for network-quality video is about 45 megabits per second (mbps). Recommendation 601 of the International Radio Consultative Committee (CCIR) calls for a 216 mbps bandwidth for video objects. A video object based on the HDTV (High Definition Television) quality images requires approximately a 700 mbps bandwidth. Compare these bandwidth requirements with the typical 10 mbps bandwidth of a magnetic disk drive, which is not expected to increase significantly in the near future. Currently, there are several ways to support continuous display of these objects: 1) sacrifice the quality of the data by using either a lossy compression technique or a low resolution device, 2) employ the aggregate bandwidth of several disk drives by declustering an object across multiple disks [2], and 3) use a combination of these two techniques. Lossy compression techniques encode data into a form that consumes a relatively small amount of space, however, when the data is decoded, it yields a representation similar to the original (some loss of data). While it is effective, there are applications that cannot tolerate loss of data. As an example consider the video signals collected from space. This data may not be compressed using a lossy compression technique. Otherwise, the scientists who later uncompress and analyze the data run the risk of either observing phenomena that may not exist due to a slight change in data or miss important observations due to some loss of data.
Shahram Ghandeharizadeh, Luis Ramos
SIGMETRICS2
1993 Continuous Retrieval of Multimedia Data Using Parallelism
abstract
Most implementations of workstation-based multimedia information systems cannot support a continuous display of high resolution audio and video data and suffer from frequent disruptions and delays termed hiccups. This is due to the low I/O bandwidth of the current disk technology, the high bandwidth requirement of multimedia objects, and the large size of these objects, which requires them to be almost always disk resident. A parallel multimedia information system and the key technical ideas that enable it to support a real-time display of multimedia objects are described. In this system, a multimedia object across several disk drives is declustered, enabling the system to utilize the aggregate bandwidth of multiple disks to retrieve an object in real-time. Then, the workload of an application is distributed evenly across the disk drives to maximize the processing capability of the system. To support simultaneous display of several multimedia objects for different users, two alternative approaches are described. The first approach multitasks a disk drive among several requests while the second replicates the data and dedicates resources to each individual request. The trade-offs associated with each approach are investigated using a simulation model.>
Shahram Ghandeharizadeh, Luis Ramos
IEEE Trans. Knowl. Data Eng.2
1991 Object Placement in Parallel Hypermedia Systems
Shahram Ghandeharizadeh, Luis Ramos, Zubair Asad, Waheed Qureshi
VLDB2