Flexible querying using structural and event based multimodal video data model

Date

2006-01-01

Author

Oztarak, Hakan
Yazıcı, Adnan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

79
views

0
downloads

Investments on multimedia technology enable us to store many more reflections of the real world in digital world as videos so that we carry a lot of information to the digital world directly. In order to store and efficiently query this information, a video database system (VDBS) is necessary. We propose a structural, event based and multimodal (SEBM) video data model which supports three different modalities that are visual, auditory and textual modalities for VDBSs and we can dissolve these three modalities within a single SEBM model. We answer the content-based, spatio-temporal and fuzzy queries of the user by using SEBM video data model more easily, since SEBM stores the video data as the way that user interprets the real world data. We follow divide and conquer technique when answering very complicated queries. We give the algorithms for querying on SEBM and try them on an implemented SEBM prototype system.

Subject Keywords

Video data, Complex query, Video object, Spatial query, Semantic place

URI

https://hdl.handle.net/11511/54981

Journal

FLEXIBLE QUERY ANSWERING SYSTEMS, PROCEEDINGS

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

Structural and event based multimodal video data modeling Öztarak, Hakan; Yazıcı, Adnan; Department of Computer Engineering (2005) Investments on multimedia technology enable us to store many more reflections of the real world in digital world as videos. By recording videos about real world entities, we carry a lot of information to the digital world directly. In order to store and efficiently query this information, a video database system (VDBS) is necessary. In this thesis work, we propose a structural, event based and multimodal (SEBM) video data model for VDBSs. SEBM video data model supports three different modalities that are vi...
Joint Virtual Machine Embedding and Wireless Data Center Topology Management Bütün, Beyza; Onur, Ertan; Department of Computer Engineering (2022-5-10) With emerging technologies such as the Internet of Things and 5G, generated data grows enormously. Hence, Data Center Networks (DCNs) have an important duty to store and process a significant amount of data, which makes them a critical component of the network. To meet the massive amount of traffic demands, wired DCNs need to deploy large numbers of servers and power-hungry switches, and huge lengths of wires. An enormous increase in the usage of cables causes high cabling complexity and cost while deployin...
Modeling Relations of Attitudes towards Technology Use Technology Competencies Ownership and Experiences to TPACKSelfEfficacy Yerdelen Damar, Sevda; Aydın, Sevgi; Boz, Yezdan (2015-04-11) This study modeled the relations of attitudes towards technology use, technology ownership, technology competency, and experience to self-efficacy of technological pedagogical content knowledge (TPACK-S). The study also investigated inter-relations among attitudes towards technology use, technology ownership, technology competency, and experience The participants of the study were 665 elementary pre-service science teachers (467 Females, 198 Males) from 7 colleges. The proposed model designed based on educa...
Multi-modal learning with generalizable nonlinear dimensionality reduction Kaya, Semih; Vural, Elif; Department of Electrical and Electronics Engineering (2019) Thanks to significant advancements in information technologies, people can acquire various types of data from the universe. This data may include multiple features in different domains. Widespread machine learning methods benefit from distinctive features of data to reach desired outputs. Numerous studies demonstrate that machine learning algorithms that make use of multi-modal representations of data have more potential than methods with single modal structure. This potential comes from the mutual agreemen...
BIG DATA FOR INDUSTRY 4.0: A CONCEPTUAL FRAMEWORK Gökalp, Mert Onuralp; Kayabay, Kerem; Eren, Pekin Erhan; Koçyiğit, Altan (2016-12-17) Exponential growth in data volume originating from Internet of Things sources and information services drives the industry to develop new models and distributed tools to handle big data. In order to achieve strategic advantages, effective use of these tools and integrating results to their business processes are critical for enterprises. While there is an abundance of tools available in the market, they are underutilized by organizations due to their complexities. Deployment and usage of big data analysis t...

Citation Formats

H. Oztarak and A. Yazıcı, “Flexible querying using structural and event based multimodal video data model,” FLEXIBLE QUERY ANSWERING SYSTEMS, PROCEEDINGS, pp. 75–86, 2006, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/54981.