This document discusses approaches for annotating heterogeneous data, with a focus on applications in transportation domains. It defines annotation as tagging or labeling data with metadata. Heterogeneous data comes from different sources and formats and at different granularities. Annotation can help with data integration, search, and addressing issues from diverse data schemas. The document reviews manual, semi-automated, and automated annotation techniques, and provides examples of rule-based and training dataset driven annotation. It also discusses using annotation for traffic data analysis like time estimation and accident avoidance. Overall, the document provides an overview of heterogeneous data annotation with a transportation domain application focus.

1. Heterogeneous Data Annotation Approaches
(Application in Transportation Domain)
Presented by: Yomna M. I. Hassan
Submitted to : Dr. Abeer ElKorany

2. Content
 What is Annotation?
 Why Annotation? (Motivation)
 Related Research Tracks
 Heterogeneous Data Types
 Why Focus on Heterogeneous Data?
 Annotation Techniques
 Research on Traffic Data
 Next Steps

3. What is Annotation
 Annotation = Tagging =Labeling (In Research)
 Associating metadata with content
 Simple keywords
 Tags are either (descriptive, structural, or
administrative “like license numbers), ..
 Types of Annotation:
• Content-dependent
• Content-independent:
such as data location,
data format, authorship etc. [1]

4. Why Annotation? (Motivation)
• Data integration and unifying
• Facilitation of search and data retrieval
• Failure of normal data integration methods such as
indexing (as database has no standard schema like
structure)[1]

5. Related Research tracks within semantics
• Mobile business intelligence
• Interoperability of semantic data
• Scalability of semantic web methods and tools

6. Heterogonous Data Types
• Heterogonous in sources
• Heterogonous in formats
• Heterogonous in granularity

7. Why Focus on Heterogeneous Data?
 Web 2.0 and the expansion of user involvement in data
generation
 Increased confidence and accuracy and reduced
ambiguity.
 Increased robustness: one sensor can contribute
information where others are unavailable, inoperative, or
ineffective.
 Enhanced spatial and temporal coverage: one sensor
can work when or where another sensor cannot
 Decreased costs: (a) a suite of ‘average’ sensors can
achieve the same level of performance as a single,
highly-reliable sensor and at a significantly lower cost,
and (b) fewer sensors may be required to obtain a (for a
particular application) sufficient picture of the system
state.

8. Annotation Techniques
Annotation
Manual
Semi-Automated
Automated

9. Basic tagging system [2]

10. Generalized Framework for Heterogeneous
Data Annotation
 Format unification
 Domain related extraction
 Annotation (Labeling) based on available information

11. Examples on Automated Tagging Research
• Automated Tagging through training dataset
• Rule-based annotation
• Semi automated Annotation Framework
• Other Examples

12. Automated Tagging through training dataset
[3]
• Framework for real-time tag recommendation. The
tagged training documents are treated as triplets of
(words, docs, tags)
• A two-way Poisson Mixture Model (PMM) is
proposed to model the document distribution into
mixture components within each cluster and
aggregate words into word clusters simultaneously.
• A new document is classified by the mixture model
based on its posterior probabilities so that tags are
recommended according to their ranks.

13. Rule-based annotation [4]
 Xml description of each sensor
 Matching with domain ontology used done through a
rule-based approach

14. Semi automated Annotation Framework [5]
 Was used in a museum shared system
 Terminology: turn from literal values to concepts
 Annombile (automate annotation): Based on feature
matching in XML with domain ontology.

15. Other Examples[6]
 Annotation for gene prediction
 Spatial Annotation (Image matching based on
location within body structure)
 Textual annotation (by word pattern matching)

16. Research on Traffic Data
 Annotation can be helpful for different types of
traffic analysis such as : traffic time estimation,
accident avoidance and notification

17. HMM for automatic annotation of traffic
trajectories [8]
 Algorithms for integrating information from geographic objects (with
the spatial extent of point, line or regions)
 HMM for semantic annotation of stops.
 The Trajectory : Sequence of (x,y,t) points
 A sequence of stops is computed and forms the real observation (O)
 The exact POI data are the superficial hidden states, whilst the POI
categories are the real hidden states that we are interested in.
 Our goal is to identify the real hidden states and use them to
annotate the stops.

18. Next Steps
 Focus on Image Annotation
 Potential challenges faced within Image annotation
1. Handling difference in format
2. Handling difference in information details available with
the image
3. Implicit details ( For example: Camera positioning,
zooming, etc…)
 Look into details related to transportation systems

19. Thank you

20. References
1- Gertz, Michael. "Data annotation in collaborative research environments."Workshop on
Data Derivation and Provenance. 2002.
2- Kim, Hak Lae, et al. "Review and alignment of tag ontologies for semantically-linked
data in collaborative tagging spaces." Semantic Computing, 2008 IEEE International
Conference on. IEEE, 2008.
3- Song, Yang, et al. "Real-time automatic tag recommendation." Proceedings of the 31st
annual international ACM SIGIR conference on Research and development in information
retrieval. ACM, 2008.
4- Moraru, Alexandra, Carolina Fortuna, and Dunja Mladenić. "Using semantic annotation
for knowledge extraction from geographically distributed and heterogeneous sensor
data." 4th SensorKDD. ACM (2010).
5- Hyvönen, Eero, Mirva Salminen, and Miikka Junnila. "Annotation of heterogeneous
database content for the semantic web." Proceedings of the 4th International Workshop
on Knowledge Markup and Semantic Annotation (SemAnnot 2004), Hiroshima, Japan.
2004.
6- Richardson, Lorna, et al. "EMAGE mouse embryo spatial gene expression database:
2010 update." Nucleic acids research 38.suppl 1 (2010): D703-D709.
7- Ou, Qing. Fusing Heterogeneous Traffic Data: Parsimonious Approaches Using Data-
data Consistency. Netherlands TRAIL Research School, 2011.
8- Yan, Zhixian, et al. "SeMiTri: a framework for semantic annotation of heterogeneous
trajectories." Proceedings of the 14th international conference on extending database
technology. ACM, 2011.