Information Sharing Mechanism among Mobile Agents In Ad-hoc Network Environment and Its Applications

Mobile agents are programs that can move from one site to another in a network with their data and states. Mobile agents are expected to be an essential tool in pervasive computing. In multi platform environment, it is important to communicate with mobile agents only using their universal or logical name not using their physical locations. More, in an ad-hoc network environment, an agent can migrate autonomously and communicate with other agents on demand. It is difficult that mobile agent grasps the position information on other agents correctly each other, because mobile agent processes a task while moving a network successively. In order to realize on-demand mutual communication among mobile agents without any centralized servers, we propose a new information sharing mechanism within mobile agents. In this paper, we present a new information sharing mechanism within mobile agents. The method is a complete peer based and requires no agent servers to manage mobile agent locations. Therefore, a mobile agent can get another mobile agent, communicate with it and shares information stored in the agent without any knowledge of the location of the target mobile agent. The basic idea of the mechanism is an introduction of Agent Ring, Agent Chain and Shadow Agent. With this mechanism, each agent can communicate with other agents in a server-less environment, which is suitable for ad-hoc agent network and an agent system can manage agents search and communications efficiently

Temporal Data Management

In this paper we develop a framework for the support of temporal data. The concept of a time sequence is introduced, and shown to be an important fundamental concept for representing the semantics of temporal data and for efficient physical organization. We discuss properties of time sequences that allow the treatment of such sequences in a uniform fashion. These properties are exploited in order to design efficient physical data structures and access methods for time sequences. We also describe operations over time sequences, and show their power to manipulate temporal data.

Similarities of Frequent Following Patterns and Social Entities

AbstractSocial network sites such as Twitter and Facebook are used for sharing knowledge and information among users. As social networks grow larger, it becomes difficult for a user to find frequently followed group of social entities. Recently, the frequent following pattern (FFP) mining concept and method were proposed to extract patterns of the relationship between a set of following entities and their most frequently followed entities. In this paper, we propose two similarity definitions: FFP similarity and FFP-based Entity (FbE) similarity. These similarities can be used to recommend a new appropriate social entity to a “read-only-user”. In other words, these similarities can be defined only with followed-and-following (F-F) relationships and without additional information such as entity characteristics or entity access logs. To the best of our knowledge, this is the first attempt to define these similarity definitions for social entity recommendations. Some examples show the effectiveness of our similarity definitions by checking their satisfaction of established requirement

Modified Conditional Restricted Boltzmann Machines for Query Recommendation in Digital Archives

Digital archives (DAs) usually store diverse expert-level materials. Nowadays, access to DAs is increasing for non-expert users, However, they might have difficulties formulating appropriate search queries to find the necessary information. In response to this problem, we propose a query log-based query recommendation algorithm that provides expert knowledge to non-expert users, thus supporting their information seeking in DAs. The use case considered is one where after users enter some general queries, they will be recommended semantically similar expert-level queries in the query logs. The proposed modified conditional restricted Boltzmann machines (M-CRBMs) are capable of utilizing the rich metadata in DAs, thereby alleviating the sparsity problem that conventional restricted Boltzmann machines (RBMs) will face. Additionally, compared with other CRBM models, we drop a large number of model weights. In the experiments, the M-CRBMs outperform the conventional RBMs when using appropriate metadata, and we find that the recommendation results are relevant to the metadata fields that are used in M-CRBMs. Through experiments on the Europeana dataset, we also demonstrate the versatility and scalability of our proposed model