Abstract: Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

Abstract: A diagnosis system for an adaptive signal control system in a network, the diagnosis system including a traffic state identification device configured to estimate a traffic state describing a supply-demand mismatch by identifying a relationship between real time data feed from a sensor and a control strategy of the adaptive signal control system and a network transition model device configured to diagnose the supply-demand mismatch and an evolution of the supply-demand mismatch on a network level based on the relationship and infrastructure data of the network.

Abstract: Embodiments for automatic feature learning for predictive modelling in a computing environment by a processor. A first table and a second table are joined based on an edge between the first table and the second table defined by an entity graph thereby creating a resulting joined table that is connected by a column of data. The resulting joined table is used as an input into one or more neural network operations that transform the resulting joined table to one or more features to predict a target variable.

Abstract: Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

Abstract: Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

Abstract: A diagnosis system for an adaptive signal control system in a network, the diagnosis system including a traffic state identification device configured to estimate a traffic state describing a supply-demand mismatch by identifying a relationship between real time data feed from a sensor and a control strategy of the adaptive signal control system and a network transition model device configured to diagnose the supply-demand mismatch and an evolution of the supply-demand mismatch on a network level based on the relationship and infrastructure data of the network.

Abstract: A diagnosis system for an adaptive signal control system in a network, the diagnosis system including a traffic state identification device configured to estimate a traffic state describing a supply-demand mismatch by identifying a relationship between real time data feed from a sensor and a control strategy of said adaptive signal control system and a network transition model device configured to diagnose the supply-demand mismatch and an evolution of the supply-demand mismatch on a network level based on said relationship and infrastructure data of the network.

Abstract: A diagnosis system for an adaptive signal control system in a network, the diagnosis system including a traffic state identification device configured to estimate a traffic state describing a supply-demand mismatch by identifying a relationship between real time data feed from a sensor and a control strategy of said adaptive signal control system and a network transition model device configured to diagnose the supply-demand mismatch and an evolution of the supply-demand mismatch on a network level based on said relationship and infrastructure data of the network.

Abstract: A diagnosis system for an adaptive signal control system in a network, the diagnosis system including a traffic state identification device configured to estimate a traffic state describing a supply-demand mismatch by identifying a relationship between real time data feed from a sensor and a control strategy of said adaptive signal control system and a network transition model device configured to diagnose the supply-demand mismatch and an evolution of the supply-demand mismatch on a network level based on said relationship and infrastructure data of the network.