A computing device is coupled to a display device, and includes a data monitoring software application program executing on a processor within a data monitoring system. Via the data monitoring software application program, various techniques are performed for generating user interfaces for data monitoring and event response. In a first technique, the data monitoring software application program displays a user interface that includes a first region including a data visualization and a second region including one or more images of a video stream. In a second technique, the data monitoring software application program generates a user interface associated with an event, receive an input corresponding to interaction with a user interface element in the user interface, and initiates an event channel associated with the event in response to the input.

Systems and methods are provided for dynamic configuration of interactive controls available on a dashboard. Interactive controls may be dynamically configured by manipulating network resource address information for a network resource that provides a dashboard, for example using query string parameters. For example, a dashboard that displays one type, source, or summary of information can be dynamically configured to allow interactive selection and display of another type, source, or summary of information depending on values passed in the network resource address information for the dashboard network resource.

A system and method for visualizing and querying high dimensional data to a user. The system includes a user device, a data-pearls visualization and querying server. The server obtains the high dimensional data from the user device associated with user. The server generates data clusters and sub-divides the data clusters into non-overlapping subsets of data-pearls using a clustering technique. The server selects a shape for each data-pearl by comparing a distance between centroid of a data-pearl and a farthest point from a determined centroid using L.sub.p norm distance measures. The server configures each data-pearl in a three-dimensional plot. The server enables the user to visualize the data-pearls on a screen of the user device. The server queries data based on a query using data dimension technique. The server dimensions data related to the query through determined classifiers based on filtered data after pruning unrelated data to the query.

Systems and methods are provided for filtering vehicle search results for an upcoming trip are described. In one example, a vehicle access platform receives a search query to view vehicles for an upcoming trip. The vehicle access platform includes an adverse outcome prediction engine and an access controller. The adverse outcome prediction engine predicts an outcome of an upcoming trip based on at least one of user information, vehicle information, or trip information. The access controller controls access, by the vehicle access platform, to available vehicles for an upcoming trip by returning to the client device filtered search results for a subset of the available vehicles that does not include prevented vehicles.

A data server may support segment identification based on a selected user profile. For example, a user may select a user profile as the basis for identifying a segment of additional user profiles. The server may identify attributes associated with the selected user identifier and generate an expression based on the identified subset. The expression may include a normalization function corresponding to at least one attribute. The normalization function may identify correlated attribute values for an attribute associated with the selected user profile. The data server may query a data storage system to identify the additional user profiles based on the expression. The data server may also support user defined Boolean expressions such that the expression is used to identify user identifiers associated with a first attribute and a second attribute.

A method for providing optimized support for transactional processing and analytical processing with minimal memory footprint may include storing, on a data page in a disk of a database system, a portion of one or more columns of data from a database table. A metadata associated with the data page may be stored on a metadata page in the disk of the database system. The metadata may include one or more byte ranges on the data page at which the portion of the one or more columns of data is stored. The database system may execute one or more queries by accessing, based at least on the metadata associated with the data page, a portion of the data page storing the portion of the one or more columns of data required by the one or more queries. Related systems and articles of manufacture are also provided.

Described are methods, systems and computer readable media for dynamic updating of query result displays.

Data is received that specifies a term generated by user input in a graphical user interface. Thereafter, the term is looked up in a dictionary in which there are multiple classes for terms. The term can be classified based on a first class having a top ranked effective count for the term within the dictionary when a ratio of the first class relative to a second class having a second ranked effective count for the term in the dictionary is above a pre-defined threshold. In addition, the term is classified using a machine learning model when the ratio of the first class relative to the second class is below the pre-defined threshold. Data can be provided which characterizes the classifying. Related apparatus, systems, techniques and articles are also described.

A method of identifying relationships between data collections is disclosed. Each data collection comprises a plurality of data records made up of data fields. The method comprises performing a relationship search process based on a first seed value and a second seed value. A first set of records from the data collections is identified based on the first seed value. A second set of records from the data collections is identified based on the second seed value. The process then searches for a common value across the first and second record sets, wherein the common value is a value which appears in a first field in a first record of the first record set and in a second field in a second record of the second record set, wherein the first record is from a first data collection and the second record is from a second data collection. In response to identifying the common value, an indication is output identifying a candidate relationship between the first field of the first data collection and the second field of the second data collection.

A sequence of context based search verb and search terms are selected via either touch or voice selection in a mobile wireless device and then a human articulated voice query is expanded using a culture and a world intelligence dictionary for conducting more efficient searches. Focus groups are used for populating prior query search databases for storage in the mobile wireless device that are organized by context based search terms in a mobile wireless device for efficient search.