Add to ORKGAbstract-We introduce a new, powerful query formulation formalism for complex, multivariate sequence data. The new query language, termed pattern graphs, is capable of reflecting more aspects of temporal patterns than earlier proposals. The underlying graph structure of the pattern graph makes the query intuitive to use and therefore understandable not only for the data analyst. We present algorithms to match patterns against data and demonstrate its usefulness on real data from the automobile industry. I
Graphs that evolve over time are called temporal graphs. They can be used to describe and represent ...
The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
We introduce a new, powerful query formulation formalism for complex, multivariate sequence data. Th...
An important goal of knowledge discovery is the search for patterns in data that can help explain th...
Classifying multivariate time series is often dealt with by transforming the numeric series into lab...
A temporal knowledge graph (TKG) is theoretically a temporal graph. Recently, systems have been deve...
An important goal of knowledge discovery is the search for patterns in the data that can help explai...
We study how to obtain concise descriptions of discrete multivariate sequential data. In particular,...
Temporal patterns composed of symbolic intervals are commonly formulated with Allen’s interval relat...
Abstract. Recently a new type of data source came into the focus of knowledge discovery from tempora...
We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome...
Graph data structures model relations between entities in various domains. Graph processing systems ...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
Graphs that evolve over time are called temporal graphs. They can be used to describe and represent ...
The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
We introduce a new, powerful query formulation formalism for complex, multivariate sequence data. Th...
An important goal of knowledge discovery is the search for patterns in data that can help explain th...
Classifying multivariate time series is often dealt with by transforming the numeric series into lab...
A temporal knowledge graph (TKG) is theoretically a temporal graph. Recently, systems have been deve...
An important goal of knowledge discovery is the search for patterns in the data that can help explai...
We study how to obtain concise descriptions of discrete multivariate sequential data. In particular,...
Temporal patterns composed of symbolic intervals are commonly formulated with Allen’s interval relat...
Abstract. Recently a new type of data source came into the focus of knowledge discovery from tempora...
We present PSEUDo, a visual pattern retrieval tool for multivariate time series. It aims to overcome...
Graph data structures model relations between entities in various domains. Graph processing systems ...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...
Graphs that evolve over time are called temporal graphs. They can be used to describe and represent ...
The issue addressed in this paper concerns the discovery of frequent multi-dimensional patterns from...
The need for pattern discovery in long time series data led researchers to develop algorithms for si...