Challenges in Ubiquitous Data Management

Franklin, Michael J.

doi:10.1007/3-540-44577-3_2

Cited by 47 publications

(35 citation statements)

References 10 publications

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“…With these parameters, application will be able to decide the situation and task that a user is currently performing, and adapt to changes in user needs and roles. This enables a mobile device to not only retrieve the required information, but also some other information that is highly relevant [17]. Examples: (i) a tourist wants to see a list of restaurants in a town.…”

Section: Context-dependent Queriesmentioning

confidence: 99%

Query Management for Efficient Data Access in Wireless Environment

Verma¹,

Rakhee²,

Kumari³

2011

IJCA

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With the cutting edge technology advance in wireless computers data broadcast has emerged as powerful tool for information dissemination to massive number of clients equipped with portable gadgets. In such computing system server continuously broadcast data over air channel without waiting for client's response and client retrieve only the data pertaining to him. To retrieve such broadcasted data mobile client submit a query to broadcast channel and get responded in sequential manner. Hence query plays central role in wireless communication. The queries evoked in wireless environment are much more complex and varied than that in traditional tethered environment. Owing to their complex nature handling of wireless queries is a challenge to the database and networking research community. This paper studied various query management strategies for wireless environment and evaluates them for multichannel broadcast system.

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Section: Context-dependent Queriesmentioning

confidence: 99%

Query Management for Efficient Data Access in Wireless Environment

Verma¹,

Rakhee²,

Kumari³

2011

IJCA

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“…First, sensors have serious resource constraints including battery lifetime, communication bandwidth, CPU capacity and storage [15]. Second, sensor node mobility increases the complexity of sensor data because a sensor may be in a different neighborhood at any point of time [7] [19]. Third, sensor data come in time-ordered streams over networks.…”

Section: Introductionmentioning

confidence: 99%

Online Mining in Sensor Networks

Yang

Tang

et al. 2004

Lecture Notes in Computer Science

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Abstract. Online mining in large sensor networks just starts to attract interest. Finding patterns in such an environment is both compelling and challenging. The goal of this position paper is to understand the challenges and to identify the research problems in online mining for sensor networks. As an initial step, we identify the following three problems to work on: (1) sensor data irregularities detection; (2) sensor data clustering; and (3) sensory attribute correlations discovery. We also outline our preliminary proposal of solutions to these problems.

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“…Pervasive computing environments extend the traditional concept of mobile networks [10,18]. Mobile devices in pervasive computing environments consist of hand helds, wearables, computers in vehicles, computers embedded in the physical infrastructure, and (nano) sensors.…”

Section: Introductionmentioning

confidence: 99%

Neighborhood-Consistent Transaction Management for Pervasive Computing Environments

Perich

Joshi

Yesha

et al. 2003

Lecture Notes in Computer Science

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Abstract. This paper examines the problem of transaction management in pervasive computing environments and presents a new approach to address them. We represent each entity as a mobile or static semi-autonomous device. The purpose of each device is to satisfy user queries based on its local data repository and interactions with other devices currently in its vicinity. Pervasive environments, unlike traditional mobile computing paradigm, do not differentiate between clients and servers that are located in a fixed, wired infrastructure. Consequently, we model all devices as peers. These environments also relax other assumptions made by mobile computing paradigm, such as the possibility of reconnection with a given device, support from wired infrastructure, or the presence of a global schema. These fundamental characteristics of pervasive computing environments limit the use of techniques developed for transactions in a "mobile" computing environments. We define an alternative optimistic transaction model whose main emphasis is to provide a high rate of successful transaction terminations and to maintain a neighborhood-based consistency. The model accomplishes this via the help of active witnesses and by employing an epidemic voting protocol. The advantage of our model is that it enables two or more peers to engage in a reliable and consistent transaction while in a pervasive environment without assuming that they can talk to each other via infrastructure such as base stations. The advantage of using active witnesses and an epidemic voting protocol is that transaction termination does not depend on any single point of a failure. Additionally, the use of an epidemic voting protocol does not require all involved entities to be simultaneously connected at any time and, therefore, further overcomes the dynamic nature of the environments. We present the implementation of the model and results from simulations.

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Challenges in Ubiquitous Data Management

Cited by 47 publications

References 10 publications

Query Management for Efficient Data Access in Wireless Environment

Query Management for Efficient Data Access in Wireless Environment

Online Mining in Sensor Networks

Neighborhood-Consistent Transaction Management for Pervasive Computing Environments

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