It becomes an interesting research topic to detect trend in the Internet era, where millions of data are posted online everyday. As social media, for example, blogs, forums, and micro-blogs, are prevailing, many offline events are discussed online. The discussion data, which reflects what people are interested in, is useful for detecting trend. This research proposes a design of online topic/tr...

Algorithms that enable the process of automatically mining distinct topics in document collections have become increasingly important due to their applications in many fields and the extensive growth of the number of documents in many domains. Traditionally, the task of topic discovery has been mainly addressed through algorithms that work on a snapshot view of the documents, which ignores the ...

This paper focuses on the detection of trend-based knowledge contained in sequential data. Some key concepts are redefined and an algorithm using the inertia test to dynamically partition a sequence to recognize trend partitions is also proposed. Experiment results prove the effectiveness of the algorithm, and exposing the limitation of traditional primitives. Some suggestions on parameter sele...

The aim of ontology learning is to generate domain models (semi-) automatically. We apply an ontology learning system to create domain ontologies from scratch in a monthly interval and use the resulting data to detect and analyze trends in the domain. In contrast to traditional trend analysis on the level of single terms, the application of semantic technologies allows for a more abstract and i...

Change points in time series are perceived as isolated singularities where two regular trends of a given signal do not match. The detection of such transitions is of fundamental interest for the understanding of the system's internal dynamics or external forcings. In practice observational noise makes it difficult to detect such change points in time series. In this work we elaborate on a bayes...

Domain scientists hope to address grand scientific challenges by exploring the abundance of data generated and made available through modern high-throughput techniques. However, the impact of this large volume of data is limited unless researchers can effectively assimilate the entirety of this complex information and integrate it into their daily research; interactive visualization tools are c...

The number and the size of spatial databases, e.g. for geomarketing, traffic control or environmental studies, are rapidly growing which results in an increasing need for spatial data mining. In this paper, we present new algorithms for spatial characterization and spatial trend analysis. For spatial characterization it is important that class membership of a database object is not only determi...

BACKGROUND The purpose of this study was to evaluate the effects of benzalkonium (BAK)-free travoprost monotherapy administered for 3 years on intraocular pressure and visual field performance. METHODS The intraocular pressure of 76 patients with normal tension glaucoma was monitored every 1-3 months. A Humphrey visual field test was performed every 6 months after treatment and compared with ...