This report presents a knowledge-based approach to diagnostic process monitoring. The cornerstone of this work is the representation and detection of multivariate trends in process data. The trend representation, called a trend template, denotes a time-varying pattern in multiple variables common to a diagnostic population. Each pattern contains representations for landmark events and a set of ...

Text mining is the process of exploratory text analysis either by automatic or semi-automatic means that helps finding previously unknown information. Text mining is a highly interdisciplinary research area, bringing together research insights from the fields of data mining, natural language processing, machine learning, and information retrieval. The amount of textual data available is too hug...

In section 2, a definition of “trend” is given. In section 3, it is shown how to detect a trend using an auto-associative neural network. Experimental methods and results are reported in sections 4 and 5, and concluding remarks are given in section 6. Abstract — This paper reports the results of a new neural network based trend detector. An auto-associative neural network was trained with the “...

We have written a prototype computer program called TrenDx for automated trend detection during process monitoring. The program uses a representation called trend templates that define disorders as typical patterns of relevant variables. These patterns consist of a partially ordered set of temporal intervals with uncertain endpoints. Bound to each temporal interval arc value constraints on real...

dimension in the co-citation literature, to date it has not been incorporated into the analogous process of link analysis on the Web. In this paper, we discuss several aspects and uses of the time dimension in the context of Web information retrieval. We describe the ideal case— where search engines track and store temporal data for each of the pages in their repository, assigning timestamps to...

where it is assumed that the expected value of X(t) is zero. There is no commonly accepted precise definition for trend, but it is usually spoken of as a nonrandom (deterministic) smooth function representing long-term movement or systematic variations in a series (for example, Priestley (1981) refers to a trend as “. . . a tendency to increase (or decrease) steadily over time . . . [or to] flu...

Social networks such as Twitter are considered a rich resource of information about actual world actions of all types. Several efforts have been dedicated to trend detection on Twitter i.e., the current popular topics of conver‐ sation among its users. However, despite these efforts, sentiment analysis is not taken into account. Sentiment analysis is the field of study that analyzes people’s op...

A mapping chase autoregression shape is applied to predict gold worth here. Previous works centered on prediction of the instability of gold worth to reveal the characteristics of gold market. By the way, due to the fact that the data of gold worth have high dimensionality, MCAF is suitable and able to predict gold worth more accurately rather than other mechanisms. In this paper, the MCAF is u...

background: tuberculosis (tb) is a pandemic disease. it is the second leading cause of death from infectious diseases after human immunodeficiency virus (hiv) in the world . the main objective of this paper was to determine and compare the epidemiology of tb incidence rate and its trend changes during 1990-2010 in six who regions regarding age, gender and income levels. methods: the average ann...

Generally, trend detection algorithms over the data stream require expert assistance in some form. We present an unsupervised multiscale data stream algorithm which detects trends for evolving time series based on a data driver data stream. The raw stream data clustering algorithm is incremental, space dilating and has linear time complexity. The evolving stream is incrementally explored on a n...