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Interania

Glossary

We realize that we use a lot of terms that aren't obvious to new users; the Interana glossary is here to give you an intro to some of the terms we use.

Concepts and building blocks

Event
An action taken by an actor (for example, a user or sensor). An event can be anything: a web conversion, an online purchase, a click on a web page, a step tracked on a wearable device, playing a song, a sensor reading, swipes on a mobile app, level up in a game, or an email click. Each event becomes a row of data. Each event can have many different attributes, but will always have a timestamp and an actor.
Actor
Anything that generates or participates in an event. Interana analyzes the behavior of actors of any kind, not just “users.” Actors on digital services may be people who access your service directly - who may be more than one kind of user. For example, Interana easily analyzes the behavior of users on different sides of two-sided marketplaces - like riders and drivers in a ridesharing service. Actors can also be real or virtual things - like devices, topics, accounts or “bots” that can also be thought to “behave” and about whom you may have the same kinds of journey, metrics, funnel and behavioral segmentation questions that you have about people. You have the ability to define multiple actors in the same data so that you can look at behavior from different points of view.
Behavior
A series of chronological events in relation to a particular actor or cohort. The order and number of events is important, as well as the elapsed time between them. For example, we could collect chronological data from a series of events in which a shopper purchases a blue sweater within one day after following a link from an advertisement placed on a third-party website.
Shard key
A column in your dataset that represents an important entity that the event is about. For example, a user or a device, which you'll use to construct behavioral queries (cohorts, sessions, and funnels).
Cohort
Cohorts segment users based on their behavior and attributes within a time period, enabling both behavioral and demographic segmentation.
Session
Sessions divide and filter actor journeys into sub-series of events. Interana then computes statistics on session counts, durations and numbers of events. A session is a sequence of consecutive events associated with a single actor. A session is identified as all events that occur during a period of activity bounded by periods of inactivity.
Metric
Metrics calculate summary statistics on any values in sets of events for filters, time periods, actors’ journeys, sessions and more. With Interana, you can intuitively develop complex metrics based on behavior, such as conversion rates, retention rates, or users’ average time spent between specified actions, to understand your business processes and complex distributions of different types of end users.
Funnel
Funnels specify steps to match actor journeys against any expected event flow and calculate statistics on how many actors completed each step. Funnels break down actor journeys into sub-series of events. Unlike sessions, they are defined by matching steps (specific events) rather than timeout. Funnels can also be used to find paths to, from, and between particular steps.
Named expressions
The collective term for metrics, sessions, cohorts, and funnels in Interana. 
Virtual columns
A useful abstraction is to think of certain query building blocks as creating virtual columns in the database. The building block is simply a definition of what to compute when the virtual columns are used in a query. When the query is run, Interana enriches rows of stored data with additional values that correspond to the virtual columns. Interana also uses virtual columns to pass intermediate results for multi-pass queries.
Multi-pass queries
Not all queries can be satisfied with a single pass across the data. Sometimes, the query requires the data to be scanned multiple times, with the results of a previous pass acting as input to the next pass. The query planner figures this all out as part of instructing the data nodes on what to do. Multi-pass queries use the concept of virtual columns and annotating rows of data to pass information between passes
Merge server
We call the process that combines results from individual data nodes the Merge server. Each data node is responsible for a subset of actors. The query planner determines what information needs to get collected and in which order. It then executes the plan by asking the data nodes to do the scanning and analysis. Data nodes all run the required scan engine in parallel to scan and analyze data for the actors they hold. They then forward the results back to the merge server, which combines all the results from the individual nodes. 
Scan engines
Scan engines are the Interana processes which analyze streams of data during scans. Scan engines come in two flavors: aggregation and annotation. Aggregation engines apply a function that results in a single value. Annotating engines result in an array of values where every array row is associated with a row of data in the analyzed time interval. These annotations can be treated as virtual columns that enrich the recorded event data and either contain the desired query results or are used as input for further passes over the data.

Interface elements

Visual Explorer
The Visual Explorer is the core portal into your data, the primary way to ask ad-hoc questions in Interana. Here you can build queries and select from different visualizations to drill down into your data.
Navigation bar
Use the icons on the left side of Interana to access features that you can use to expand your queries.
Dashboard
Dashboards are a shared workspace to save and follow queries built through features tabs. As you explore your data, you can pin the results to a dashboard and share them with your team.
Query builder
The set of controls on the left side of the screen in the Explorer tab. Use the controls in the query builder to create and run your queries. 
View
The visualization of query results in the form of a chart, graph, table, or number, together with settings determined by Interana to optimize that visualization. Using views, you can visualize your results flexibly on the fly, and can alter your query directly through the chart controls within the view.  
Measure
You can measure almost any count or aggregation over a data column (such as an average or median), metrics that you create, or metrics that Interana creates automatically when you create named expressions (specifically, when you create sessions and funnels).
Filters
Use filters to determine which data to include in the results. Use Simple filters to quickly select one or more columns or metrics to use as a filter. Use the Advanced filters to create a more complex, text-based filter.
Visualization window
Presents the results of your exploration through a View. It includes a visualization of your query results, a text-based translation of the query you formed in the Visual Query Builder, statistics about your query, and tools for adjusting the visual display and working with the data. The type of visualization (chart, graph, etc.) and the tools available depend on the View selected in the Visual Query Builder.
Time scrubber
Use the Time Scrubber below the chart to adjust the time period being analyzed. Click and drag to select a period of time.
Query statistics
The small circle in the upper left hand corner of any Visualization window displays the percentage of rows scanned that matched the query. Hovering over this circle opens a pop-up that presents detailed statistics about the query.
Chart controls
The query builder lets you change the parameters of your query. With Chart Controls, you can adjust each view at a granular level across multiple dimensions. Results are calculated on the fly, making it easy to sample or not sample data, add another y-axis, plot splines, lines or dots and control time window, ranges and chart resolution. Different views give you different ways to adjust how your query results are presented.  
Query history
The visual query history is a "breadcrumb trail" of your previous analyses at the bottom of the window. As you perform queries, previous queries appear in condensed form below the Visualization Window. Click the up arrow to go back to a query, or click the diagonal arrows to expand the image in the history (this is useful when you're looking for a specific query to work with).
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