Abstract: Detection devices for detecting one or more target analytes such as volatile organic compounds (VOCs) may include a base and a sensor module coupleable to the base and including at least one electrochemical sensor, where the electrochemical sensor includes an electrode and an ionic liquid (e.g., room temperature ionic liquid) that is arranged on the electrode and specific to a target analyte. In some variations, at least one cavity specific to the target analyte is formed within the ionic liquid in response to the electrochemical sensor receiving an input signal.

Abstract: Systems and methods are described for a streaming data processing system that defers processing of some data based on a determined importance of the data. A streaming data processing system can ingest a data stream that contains multiple events, and can extract data field values from individual events and process the data field values to determine event importance. The streaming data processing system can then do further processing and indexing of high importance events, and can generate a storage prefix for each low importance event that determines where to store the low importance event in a data storage system. The streaming data processing system can then process queries by retrieving the indexed high importance events, and can extract the data field values from a high importance event to determine the storage prefix for retrieving corresponding low importance events from the data storage system.

Abstract: Detection devices for detecting one or more target analytes such as volatile organic compounds (VOCs) may include a base and a sensor module coupleable to the base and including at least one electrochemical sensor, where the electrochemical sensor includes an electrode and an ionic liquid (e.g., room temperature ionic liquid) that is arranged on the electrode and specific to a target analyte. In some variations, at least one cavity specific to the target analyte is formed within the ionic liquid in response to the electrochemical sensor receiving an input signal.

Abstract: The present technique discloses a method and system for creating target entity profile. Data related to target entity are collected from various sources. User configures, through genome management console of genome query engine, gene block—dimension matrix which defines relationship between gene blocks and dimensions present in the collected data. Based on gene block—dimension matrix, gene blocks are loaded. User further configures genome attributes of their choice for which data needs to be analyzed. Data corresponding to configured genome attributes are extracted from gene blocks and from these extracted data, genome attributes are calculated based on computation type.

Abstract: The present technique discloses a method and system for creating target entity profile. Data related to target entity are collected from various sources. User configures, through genome management console of genome query engine, gene block—dimension matrix which defines relationship between gene blocks and dimensions present in the collected data. Based on gene block—dimension matrix, gene blocks are loaded. User further configures genome attributes of their choice for which data needs to be analyzed. Data corresponding to configured genome attributes are extracted from gene blocks and from these extracted data, genome attributes are calculated based on computation type.