Formal model of time point-based sequential data for OLAP-like analysis

Abstract

Abstract. Numerous nowadays applications generate huge sets of data, whose natural feature is order, e.g,. sensor installations, RFID devices, workflow systems, Website monitors, health care applications. By analyzing the data and their order dependencies one can acquire new knowledge. However, nowadays commercial BI technologies and research prototypes allow to analyze mostly set oriented data, neglecting their order (sequential) dependencies. Few approaches to analyzing data of sequential nature have been proposed so far and all of them lack a comprehensive data model being able to represent and analyze sequential dependencies. In this paper, we propose a formal model for time point-based sequential data. The main elements of this model include an event and a sequence of events. Measures are associated with events and sequences. Measures are analyzed in the context set up by dimensions in an OLAP-like manner by means of the set of operations. The operations in our model are categorized as: operations on sequences, on dimensions, general operations, and analytical functions.

Application of graph databases for transport purposes

“…Address of the object is thus defined as a product of record size and identifier number. Accordingly, the complexity of node or edge search operation by identifier does not depend on the number of objects in a database and it is equal to O (1). The operation of node or edge adding consists of creating a new record at the end of a database file.…”

“…The cost of such operation is linear O(1) and does not depend on a graph size. The operation of deleting consists in setting an appropriate flag in object record and does not cause the change of data file A. Czerepicki size, thus its complexity also is equal to O (1). In accordance to the requirement of referential integrity of a graph database, a node can be deleted, if there are no edges linked to it, whereas, an edge can always be deleted.…”

“…Its complexity therefore is equal to where -number of object attributes. Since k number is not large, in most cases, the operation complexity of attribute search can be assumed as O (1). The same complexity is also characteristic for adding or deleting attribute operation.…”

“…in the form of an entity relationship diagram created in crow's foot notation [16]. (1) where is a general travel time which is a sum of constituent times of travelling parts of the route of one line ∈ , waiting times connected with a change to another line at the same stop.…”

“…and making it available to end users or storing into data warehouse for discovering and analysing frequently used routes, bottlenecks etc. through advanced on-line analytical processing [1]. However, it should be mentioned that more and more often the recipient of this information is an ordinary user of e.g.…”