Abstract: A method for performing a dynamic spatial search for points of interest proximal to a path of travel of a moving user. As the user moves along a path, a search is regularly conducted to provide updated information about points of interest for the user. The method includes automatically detecting a position of the user. One or more attributes of the user's movement and/or of a vicinity around the position of the user are detected. To provide the updated search information a new search region is determined based on the user's position and the one or more detected attributes. The new search region is compared to the previously determined search region to determine an overlap region that the two have in common. Search results from the overlap region are reused as part of point of interest search results for the current search region. New point of interest search results are calculated only for a portion of the new search region outside of the overlap region.

Abstract: A method for processing two different types of geographic address data sets to establish candidate matched data includes entering address data into the system and accessing a point level address data set. A determination is made if the point level data set contains an exact match to the entered address data. Any exact address data match from the point level data set is output. Candidate address data matches from the point level data set are saved when no exact match is determined as existing in the point level data set. A centerline address data set is accessed and a determination is made of candidate address data matches from the centerline address data set. Candidate address data from both the point level data set and the centerline data set are scored and matched. A system for geocoding and address hygiene includes a processing system with an input and an output system coupled to said processing system. A point level data set is coupled to the processing system.

Abstract: A method and apparatus for performing forecasting using time series data are described. In one embodiment, the method includes designating a last portion of the time series data as a base pattern, searching the remaining portion of the time series data for candidate patterns using the base pattern, and identifying candidate forecasts corresponding to the candidate patterns. The method further includes calculating a set of final forecast values based on the candidate forecasts.

Abstract: A method and apparatus for creating a lineage for a data field is disclosed. The method may include creating a unique identifier for a data field. Also, a record of the origin of data is created when the data is inserted into the data field; however this is not required. The record of the origin of data is associated with the unique identifier for the data field. The lineage for the data field may be viewed. In so doing, the transformations to the data field may be shown.

Abstract: A method for facilitating display of content in a browser window includes receiving a user request for content, determining that the requested content cannot be displayed in a browser window using a single page, and generating code which, when executed, causes the display of the requested content to be divided into multiple frames. Each frame displays a portion of the requested content. The frames displayed in the browser window appear to the user as a single page containing the requested content.

Abstract: A method and apparatus for performing forecasting using time series data are described. In one embodiment, the method includes designating a last portion of the time series data as a base pattern, searching the remaining portion of the time series data for candidate patterns using the base pattern, and identifying candidate forecasts corresponding to the candidate patterns. The method further includes calculating a set of final forecast values based on the candidate forecasts.

Abstract: A method and apparatus for performing forecasting using time series data are described. In one embodiment, the method includes designating a last portion of the time series data as a base pattern, searching the remaining portion of the time series data for candidate patterns using the base pattern, and identifying candidate forecasts corresponding to the candidate patterns. The method further includes calculating a set of final forecast values based on the candidate forecasts.