Add to ORKGAbstract: Correlating event streams or development paths of observed behavior that involves disparate types of data is a common problem in many applications including biomedical and clinical diagnosis systems. We present a new problem formulation of specifying feature space with heterogeneous dissimilarity measures, and trying to find similar time series given these (expert) user-specified heterogeneities, both within the same feature and as combinations across multiple features. By allowing domain experts to describe their feature spaces more accurately in this fashion, query matches are better suited to the domain experts ’ needs. The presented work augments the existing research of finding local similarity areas and overall patterns in t...
Abstract. In this paper we propose a solution to the similarity measur-ing for heterogenous data. Th...
Similarity-based querying of time series data can be categorized as pattern existence queries and sh...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
Important task when trying to find patterns in applications involving mining different types of data...
Time series prediction and control may involve the study of massive data archive and require some ki...
We present one of the main problems in information retrieval and data mining, which is the similarit...
AbstractTime series data are commonly used in data mining. Clustering is the most frequently used me...
Abstract. Heterogeneous information networks have attracted much attention in recent years and a key...
Efficient and effective methods of making data accessible to its consumers - be they humans or algor...
Similarity search in time series data is required in many application fields. The most prominent wor...
Insights from database research, notably in the areas of data mining and similarity search, and adva...
In many application domains, data can be represented as a series of values (time series). Examples i...
Similar entity search is the task of identifying entities that most closely resemble a given entity ...
In this work, we address the problem of similarity search in a database of uncertain spatio-temporal...
Abstract. In the recent years, our ability of collecting information rapidly increases and huge data...
Abstract. In this paper we propose a solution to the similarity measur-ing for heterogenous data. Th...
Similarity-based querying of time series data can be categorized as pattern existence queries and sh...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...
Important task when trying to find patterns in applications involving mining different types of data...
Time series prediction and control may involve the study of massive data archive and require some ki...
We present one of the main problems in information retrieval and data mining, which is the similarit...
AbstractTime series data are commonly used in data mining. Clustering is the most frequently used me...
Abstract. Heterogeneous information networks have attracted much attention in recent years and a key...
Efficient and effective methods of making data accessible to its consumers - be they humans or algor...
Similarity search in time series data is required in many application fields. The most prominent wor...
Insights from database research, notably in the areas of data mining and similarity search, and adva...
In many application domains, data can be represented as a series of values (time series). Examples i...
Similar entity search is the task of identifying entities that most closely resemble a given entity ...
In this work, we address the problem of similarity search in a database of uncertain spatio-temporal...
Abstract. In the recent years, our ability of collecting information rapidly increases and huge data...
Abstract. In this paper we propose a solution to the similarity measur-ing for heterogenous data. Th...
Similarity-based querying of time series data can be categorized as pattern existence queries and sh...
There has been huge progress in the time series domain. Every day, a large volume of time series dat...