Quality of service provision in mobile multimedia - a survey

The prevalence of multimedia applications has drastically increased the amount of multimedia data. With the drop of the hardware cost, more and more mobile devices with higher capacities are now used. The widely deployed wireless LAN and broadband wireless networks provide the ubiquitous network access for multimedia applications. Provision of Quality of Service (QoS) is challenging in mobile ad hoc networks because of the dynamic characteristics of mobile networks and the limited resources of the mobile devices. The wireless network is not reliable due to node mobility, multi-access channel and multi-hop communication. In this paper, we provide a survey of QoS provision in mobile multimedia, addressing the technologies at different network layers and cross-layer design. This paper focuses on the QoS techniques over IEEE 802.11e networks. We also provide some thoughts about the challenges and directions for future research

Comparison of Visual Datasets for Machine Learning

One of the greatest technological improvements in recent years is the rapid progress using machine learning for processing visual data. Among all factors that contribute to this development, datasets with labels play crucial roles. Several datasets are widely reused for investigating and analyzing different solutions in machine learning. Many systems, such as autonomous vehicles, rely on components using machine learning for recognizing objects. This paper compares different visual datasets and frameworks for machine learning. The comparison is both qualitative and quantitative and investigates object detection labels with respect to size, location, and contextual information. This paper also presents a new approach creating datasets using real-time, geo-tagged visual data, greatly improving the contextual information of the data. The data could be automatically labeled by cross-referencing information from other sources (such as weather)

DATA MINING AND PROBABILISTIC NETWORK-BASED MECHANISM FOR DATA WAREHOUSING

A good multirmedia database managerment systerm (MDBMS) should be able to store, retrieve, and manage rich semantic data in multimedia database syst,ems. Due to the complexity of real-world applications, the number of databases and the volumes of data in databases have increased tremendously. With the explosive growth in the amount and complexity of data, how to effectively manage the network of databases and utilize the large amount of data becomes important. For this purpose, a probabilistic network-based mechanism for constructing a federation of data warehouses and speeding up information retrieval to facilitate the functionality of an MDBMS is proposed. Our solution procedure consists of three steps. First, we build the probabilistic network by reasoning the probability distributions and mining the generalized affinity-based associations from a set of historical data collected from the network of operational databases. By doing so, the summarized and useful knowledge can be discovered. Second, we derive a siirnilarity measure method to construct a federation of data warehouses so as to reduce the number of inter-warehouse accesses required for queries. Those databases with high similarity values are placed in the same data warehouse. The similarity value is measured via a stochastic process from the mined probability distributions. Third, a second stochastic process generates a list of possible paths with respect to a given query and specifies the particular media objects over the constructed data warehouses so as to speed up multimedia query processing and information retrieval

A probabilistic network-based mechanism for multimedia database searching and data warehousing

A good multimedia database management system ( MDBMS) should be able to store, retrieve, and manage rich semantic data in multimedia database systems. Data can be stored not only in standardized databases but also in object repositories, knowledge bases, file systems, document retrieval systems, multimedia databases, and so on. Due to the complexity of real-world applications, the number of databases and the volumes of data in databases have increased tremendously. With the explosive growth in the amount and complexity of data, how to effectively manage the network of databases and utilize the large amount of data becomes important. For this purpose, a probabilistic network-based mechanism for constructing a federation of data warehouses and speeding up information retrieval to facilitate the functionality of an MDBMS is proposed. Our solution procedure consists of three steps. First, we build the probabilistic network by reasoning the probability distributions and mining the generalized affinity-based associations from a set of historical data collected from the network of operational databases. By doing so, the summarized and useful knowledge can be discovered. Second, we derive a similarity measure method to construct a federation of data warehouses so as to reduce the number of inter-warehouse accesses required for queries. Those databases with high similarity values are placed in the same data warehouse. The similarity value is measured via a stochastic process from the mined probability distributions. Third, a second stochastic process generates a list of possible paths with respect to a given query and specifies the particular media objects over the constructed data warehouses so as to speed up multimedia query processing and information retrieval. To illustrate these benefits, our approach has been implemented and empirical studies on real databases are presented. Metrics for measuring the performance of the proposed mechanism are presented and the effectiveness of the system is thereby evaluated. The empirical study results show that the probabilistic reasoning and data mining processes lead to a better federation of data warehouses and reduce the cost of query processing

Organizing a Network of Databases Using Probabilistic Reasoning

Due to the complexity of real-world applications, the number of databases and the volumes of data in databases have increased tremendously. With the ex-plosive growth in the amount and complexity of data, how to effectively organize the databases and utilize the huge amount of data becomes important. For this purpose, a probabilistic network that organizes a network of databases and manages the data in the databases is proposed in this paper. Each database is represented as a node in the probabilistic network and the affinity relations of the databases are embed-ded in the proposed Markov model mediator (MMM) mechanism. Probabilistic reasoning technique is used to formulate and derive the probability distributions for an MMM. Once the probability distributions of each MMM are generated, a stochastic process is con-ducted to calculate the similarity measures for pairs of databases. The similarity measures are transformed into the branch probabilities of the probabilistic net-work. Then, the data in the database can be managed and utilized to allow user queries for database search-ing and information retrieval. An example is included to illustrate how to model each database into an MMM and how to organize the network of databases into a probabilistic network.

Affinity Relation Discovery in Image Database Clustering and Contentbased Retrieval

In this paper, we propose a unified framework, called Markov Model Mediator (MMM), to facilitate image database clustering and to improve the query performance. The structure of the MMM framework consists of two hierarchical levels: local MMMs and integrated MMMs, which model the affinity relations among the images within a single image database and within a set of image databases, respectively, via an effective data mining process. The effectiveness and efficiency of the MMM framework for database clustering and image retrieval are demonstrated over a set of image databases which contain various numbers of images with different dimensions and concept categories

Affinity-Based Similarity Measure for Web Document Clustering

Compared to the regular documents, the major distinguishing characteristics of the Web documents is the dynamic hyper-structure. Thus, in addition to terms or keywords for regular document clustering, Web document clustering can incorporate some dynamic information such as the hyperlinks and the access patterns extracted from the user query logs. In this paper, we extend the concept of document clustering into Web document clustering by introducing the strategy of affinity-based similarity measure, which utilizes the user access patterns in determining the similarities among Web documents via a probabilistic model. Several comparison experiments are conducted using a real data set and the experimental results demonstrate that the proposed similarity measure outperforms the Cosine coefficient and the Euclidean distance method under different document clustering algorithms. 1