Shojiro Nishio

H-mine: hyper-structure mining of frequent patterns in large databases

Methods for efficient mining of frequent patterns have been studied extensively by many researchers. However, the previously proposed methods still encounter some performance bottlenecks when mining databases with different data characteristics, such as dense vs. sparse, long vs. short patterns, memory-based vs. disk-based, etc. In this study, we propose a simple and novel hyper-linked data structure, H-struct and a new mining algorithm, H-mine, which takes advantage of this data structure and dynamically adjusts links in the mining process. A distinct feature of this method is that it has very limited and precisely predictable space overhead and runs really fast in memory-based setting. Moreover it can be scaled up to very large databases by database partitioning, and when the data set becomes dense, (conditional) FP-trees can be constructed dynamically as part of the mining process. Our study shows that H-mine has high performance in various kinds of data, outperforms the previously developed algorithms in different settings, and is highly scalable in mining large databases. This study also proposes a new data mining methodology, space-preserving mining, which may have strong impact in the future development of efficient and scalable data mining methods.

A survey on communication and data management issues in mobile sensor networks

Wireless sensor networks (WSNs) which is proposed in the late 1990s have received unprecedented attention, because of their exciting potential applications in military, industrial, and civilian areas (e.g., environmental and habitat monitoring). Although WSNs have become more and more prospective in human life with the development of hardware and communication technologies, there are some natural limitations of WSNs (e.g., network connectivity, network lifetime) due to the static network style in WSNs. Moreover, more and more application scenarios require the sensors in WSNs to be mobile rather than static so as to make traditional applications in WSNs become smarter and enable some new applications. All this induce the mobile wireless sensor networks (MWSNs) which can greatly promote the development and application of WSNs. However, to the best of our knowledge, there is not a comprehensive survey about the communication and data management issues in MWSNs. In this paper,focusing on researching the communication issues and data management issues in MWSNs, we discuss different research methods regarding communication and data management in MWSNs and propose some further open research areas in MWSNs.Copyright

Mining people's trips from large scale geo-tagged photos

Photo sharing is one of the most popular Web services. Photo sharing sites provide functions to add tags and geo-tags to photos to make photo organization easy. Considering that people take photos to record something that attracts them, geo-tagged photos are a rich data source that reflects peoples memorable events associated with locations. In this paper, we focus on geo-tagged photos and propose a method to detect peoples frequent trip patterns, i.e., typical sequences of visited cities and durations of stay as well as descriptive tags that characterize the trip patterns. Our method first segments photo collections into trips and categorizes them based on their trip themes, such as visiting landmarks or communing with nature. Our method mines frequent trip patterns for each trip theme category. We crawled 5.7 million geo-tagged photos and performed photo trip pattern mining. The experimental result shows that our method outperforms other baseline methods and can correctly segment photo collections into photo trips with an accuracy of 78%. For trip categorization, our method can categorize about 80% of trips using tags and titles of photos and visited cities as features. Finally, we illustrate interesting examples of trip patterns detected from our dataset and show an application with which users can search frequent trip patterns by querying a destination, visit duration, and trip theme on the trip.

Dynamic TDMA slot assignment in ad hoc networks

In this paper we propose a TDMA slot assignment protocol to improve the channel utilization, which controls the excessive increase of unassigned slots by changing the frame length dynamically. Our proposed protocol assigns one of the unassigned slots to a node which joins the network. If there are no unassigned slots available, our proposed protocol generates unassigned slots by depriving one of the multiple slots assigned to a node, or enlarging frame length of nodes which can cause collision with each other. Moreover, by setting frame length as a power of 2 slots, our proposed protocol provides the collision-free packet transmission among nodes with different frame length. The simulation results show that our proposed protocol improves the channel utilization dramatically as compared with the conventional protocols.

Replica allocation for correlated data items in ad hoc sensor networks

To improve data accessibility in ad hoc networks, in our previous work we proposed three methods of replicating data items by considering the data access frequencies from mobile nodes to each data item and the network topology. In this paper, we extend our previously proposed methods to consider the correlation among data items. Under these extended methods, the data priority of each data item is de-fined based on the correlation among data items, and data items are replicated at mobile nodes with the data priority. We employ simulations to show that the extended methods are more efficient than the original ones.

Generalization-based data mining in object-oriented databases using an object cube model

Data mining is the discovery of knowledge and useful information from the large amounts of data stored in databases. With the increasing popularity of object-oriented database systems in advanced database applications, it is important to study the data mining methods for object-oriented databases because mining knowledge from such databases may improve understanding, organization, and utilization of the data stored there. In this paper, issues on generalization-based data mining in object-oriented databases are investigated in three aspects: (1) generalization of complex objects, (2) class-based generalization, and (3) extraction of different kinds of rules. An object cube model is proposed for class-based generalization, on-line analytical processing, and data mining. The study shows that (i) a set of sophisticated generalization operators can be constructed for generalization of complex data objects, (ii) a dimension-based class generalization mechanism can be developed for object cube construction, and (iii) sophisticated rule formation methods can be developed for extraction of different kinds of knowledge from data, including characteristic rules, discriminant rules, association rules, and classification rules. Furthermore, the application of such discovered knowledge may substantially enhance the power and flexibility of browsing databases, organizing databases and querying data and knowledge in object-oriented databases.

Energy-efficient cooperative communication for data transmission in wireless sensor networks

It is a practical challenge to provide reliable and efficient communication for data transmission in wireless sensor networks. To recover from packet losses, conventional approaches tried to use retransmission or FEC mechanisms. However, these mechanisms may introduce excessive energy overhead for reliability guarantee. By exploiting the wireless broadcast nature and the node overhearing capability, we propose a novel cooperative communication scheme EECC to improve data transmission performance for wireless sensor networks. In this scheme, cooperative reply is performed at each hop by the best-suited node elected from those that have successfully overheard the transmitted packet. EECC is not a routing protocol but rather works as an augment to minimize the impact of packet losses on network performance. Extensive analytical and experimental results confirm that our scheme is very effective in improving both energy efficiency and end-to-end delay for data transmission in lossy networks.

Path planning using a mobile anchor node based on trilateration in wireless sensor networks

In wireless sensor networks (WSNs), many applications require sensor nodes to obtain their locations. Now, the main idea in most existing localization algorithms has been that a mobile anchor node (e.g., global positioning system-equipped nodes) broadcasts its coordinates to help other unknown nodes to localize themselves while moving according to a specified trajectory. This method not only reduces the cost of WSNs but also gets high localization accuracy. In this case, a basic problem is that the path planning of the mobile anchor node should move along the trajectory to minimize the localization error and to localize the unknown nodes. In this paper, we propose a Localization algorithm with a Mobile Anchor node based on Trilateration (LMAT) in WSNs. LMAT algorithm uses a mobile anchor node to move according to trilateration trajectory in deployment area and broadcasts its current position periodically. Simulation results show that the performance of our LMAT algorithm is better than that of other similar algorithms. Copyright

Improving the extraction of bilingual terminology from Wikipedia

Research on the automatic construction of bilingual dictionaries has achieved impressive results. Bilingual dictionaries are usually constructed from parallel corpora, but since these corpora are available only for selected text domains and language pairs, the potential of other resources is being explored as well. In this article, we want to further pursue the idea of using Wikipedia as a corpus for bilingual terminology extraction. We propose a method that extracts term-translation pairs from different types of Wikipedia link information. After that, an SVM classifier trained on the features of manually labeled training data determines the correctness of unseen term-translation pairs.

A collaborative Web browsing system for multiple mobile users

In mobile computing environments, handheld devices with low functionality restrict the services provided for mobile users. We propose a new concept of collaborative browsing, where mobile users collaboratively browse Web pages designed for desktop PC. In collaborative browsing, a Web page is divided into multiple components, and each is distributed to a different device. In mobile computing environments, the number of handheld devices, their capabilities, and other conditions can vary widely amongst mobile users who want to browse content. Therefore, we developed a page partitioning method for collaborative browsing, which divides a Web page into multiple components. Moreover, we designed and implemented a collaborative Web browsing system in which users can search and browse their target information by discussing and watching partial pages displayed on multiple devices