Data and metadata challenges
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3. 2. Problem Formulation. WALSAIP Conceptual Model. Study Mechanisms for data and metadata acquisition. Data + Metadata + Processing Decision Making!. Study correspondences of data and metadata files. Raw Data Servers. Computed Data Servers. INTERNET. Data Representation

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Data and Metadata Challenges

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Data and metadata challenges



Problem Formulation

WALSAIP Conceptual Model

Study Mechanisms for data and metadata acquisition

  • Data + Metadata + Processing

  • Decision Making!

Study correspondences of data and metadata files

Raw Data


Computed Data



Data Representation


Computational Signal

Processing Systems

Signal Data


Information Rendering


Signal Conditioning


Sensor Array


Pre-processing Stage

Processing Stage

Post-processing Stage

Automated XML Schema Representations for Sensor-based Information Processing Systems

Luz V. Acabá-Cuevas – M.S. StudentProf. Domingo Rodríguez – Advisor

AIP Group, ECE Department University of Puerto Rico

Email: [email protected] Campus



Proposed Solution



JammingInterferencePower FailureEtc….


This work centers on the design and development of a Java-based XML information representation (XIR) tool for the coupling/binding representation of data and metadata entities associated with physical sensors pertaining to environmental surveillance monitoring (ESM) applications. Metadata, defined in general as data that describe data, is associated with each sensor-signal-data through a binding/coupling registry process using Extensible Markup Language (XML) format. The concept of sensor data availability in ESM is decomposed into three specific requirements for the XIR system: let users get to information in a remote manner, get access to data as soon as it is required, and enable a uniform interpretation of data among heterogeneous data sources and data destinations.



  • Design and implementation of the Information Representation Tool (XIR) tool using Java, XML, and FTP technologies for encapsulation of data and metadata files (proposed as format for information content exchange) in automated information processing systems.

  • Enable user to develop “stencils” in order to customize “XML tags” during encapsulation.

  • Information theoretic measures are used to study how the extensible markup language (XML) may serve as a means for integrating symbols and meaning (semiotics and semantics parts), from metadata, with signals and structure (syntactic part) from sensor-based raw signal-data.


Information Source










Information Destination





Figure 4. Shannon’s Theory and XML Processing

  • Proposed solution contemplates dynamic metadata management.

  • Enable data and metadata enhancement with user observations.

  • Context awareness aids in the detection, estimation, and classification of sensor-based signals acquired from ESM for the assessment and proper management of Earth’s geophysical, environmental, and ecological issues.

Figure 5. Shannon’s Theory Approach to Information Flow Study

Figure 1. WALSAIP Conceptual Model


Implementation Effort

<?xml version="1.0" ?>

- <encapsulation>

- <metadata>

- <research>

<researchName>Wide Area Large Scale Automated Information Processing</researchName>

<department>Department of Electrical and Computer Engineering</department>

<intitution>University of Puerto Rico at Mayaguez</intitution>


<contact>Domingo Rodriguez</contact>

<email>[email protected]</email>


- <sensingInfo>










<data>65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 65535 </data>


  • Analysis of current metadata management and storage formats in order to provide encapsulation support.

  • Analysis of data/metadata consumer modules within WALSAIP architecture to ensure compatibility and integration.

  • Generation algorithms to acquire plain text values non text data such as images and acoustic signals.

  • Engineering of algorithms to gather critical metadata directly from data. For example image dimensions and format.

Data and Metadata

  • Signal Data – all readings collected directly from sensors.

  • Metadata – data that describes data. Metadata is crucial to provide researchers a concrete idea of the real conditions in which data was collected. Metadata is a determinant of how the environment influenced the measurement in case of abnormal findings.

Data and Metadata Challenges

  • There is a need for proper characterization of binding/coupling relationships between data and metadata files to improve information content analysis.

  • Data should be interoperable across heterogeneous users with different data architectures, storage systems, and platforms.

  • A mechanism should be design to make data readable and understandable across heterogeneous users in automated information processing systems.

  • Lack of support for dynamic metadata management.

  • Systems need to incorporate information from “human sensors”.

Figure 6. Data and Metadata Encapsulation Example


Ongoing Work

  • Applying engineering techniques for solution design.

  • Generating source code to implement a proposed solution instantiation.

  • Identifying potential test cases to perform functional verification test after coding.

  • Integrating a proposed solution to the WALSAIP architecture.

Figure 2. Decision Making Input



[1] Manetti Luca, Terribilini Andrea, Knecht Alfredo, “Autonomous Remote Monitoring System

for Landslides”, SPIE’s 9th Annual International Symposium on Smart Structures and Materials,

2002, San Diego, CA.

[2] Nativi Stefano, Giuli Dino, Innocenti Emilio Bugli, “Interoperability for Multimedia Systems to

Support Decision-Makers in the Environment Sector” IEEE International Conference

On Multimedia Computing and Systems, Volume 2, June 1999, Pages 338-342

[3] Dong-Jun Won, Il-Yop Chung,   Joong-Moon Kim,  Seung-Il Moon,   Jang-Cheol Seo,  

Jong-Woong Choe, D Won, II-Yop Chung, J. Kim, S. Moon, J. Seo, J. Choe, “Development

of Power Quality Monitoring System with Central Processing Scheme”, Power Engineering

Society Summer Meeting, IEEE, South Korea, pp. 915-919 vol.2, 21-25 July 2002.

[4] MANTIS Project (MultimodAl Networks of In-situ Sensors):

Figure 3. Example: NERR System Data/Metadata

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