TIBCO Spotfire (for time-series)

Spotfire is a broad, general-purpose analytics offering that encompasses data visualisation, business intelligence, analytics (including AI-based predictive analytics) and data preparation; running against historic and/or real-time (streaming) data. A marketecture diagram for the product is shown in Figure 1. However, in this InBrief we are concerned specifically with its time-series capabilities when analysis of historic data is required and, as a complementary technology, the ability to combine this with geo-spatial information.

Fig 01 Spotfire marketecture

The product is available both on premises and in the cloud and, in the latter case, a managed service offering is available. Pricing is by user persona and a free 30-day trial is available.

Customer Quotes

“We’ve reduced the time by over 50% that it takes to create usable information for hospital administrators. We don’t have to crunch all this data because it’s automated.”
BroadReach Healthcare

“We developed a vessel speed and route monitoring application that analyses the vessel’s speed and distance against a complex variety of factors. The application has helped ocean carriers reduce fuel consumption by up to 3.5% over the past two years.”
CargoSmart

From a time-series perspective Spotfire works on the basis of time windows (various options) that you define. Specifically, it provides high level operators called Aggregate, Pattern, Join, Query and Gather, which can be combined and sequenced as required. Some 58 aggregate functions are built into the product, including both basic analytical functions (mean, median, standard deviation and so on) and more advanced statistical functions (slope, intercept, correlation, exponential moving average and so forth). A Java aggregate function API is available for users to define their own aggregate functions, if desired.

The Pattern operator has its own sub-language for sequential, value-based and temporal patterns, to which Boolean logic may be applied. Patterns within streams may also be detected using one or more operators in combination. The Join and Gather operators join streams either as two-way or multi-way joins respectively. Finally, the Query operator allows you to join a stream against a relational table, so that you can combine real-time and historic data. Streams may be buffered in such a way that arrival order is not material to the results of the operation so long as emission order is not material either.

Of course, for many IoT and industrial applications support for time-series analytics is not enough: you also need to combine this with location intelligence and Spotfire provides significant capabilities in this regard, supporting both geographical and non-geographical (see Figure 2) maps. Multi-layer mapping lets you zoom into successive levels of detail, for example, from the country to the state, county, city, neighbourhood, and house level. Moreover, you can choose to see whatever associations are relevant to you, with data specific to the level you are working at. The product provides worldwide address-level geocoding as well as route calculations with step-by-step directions.

Fig 02 Mapping the human body

More generally, the product uses native connectors, not just for its analytics but also for data preparation (TIBCO calls it wrangling) so that you can connect to and blend data from  a variety of relational and NoSQL databases; and to cloud environments like Amazon Redshift, Databricks, RDS, Microsoft Azure SQL Database, Google Analytics, and Salesforce.com. You can also build your own custom connectors. Natural Language Query capability – which is used throughout the product – lets you search for any data or connector.

From a more general standpoint, Spotfire supports AI/ML and predictive models developed in other TIBCO tools such as Statistica, as well as supporting languages such as R, Python and Java, and models developed in third party environments such as Spark MLlib or H20 models, or which can be imported via PMML (predictive modelling mark-up language). These can be scored on streaming data by operators in Spotfire. Also notable is the TIBCO Artefact Management Service, which supports governed deployment for these models. Once approved, then can be pushed out to Spotfire applications, or the applications can be set up to query for updated models at regular intervals, or under certain conditions. These models can be updated at runtime without requiring any application downtime.

Spatial analytics is commonplace and all the providers of streaming analytics platforms have the ability to process streaming data via time windows, in some sort of similar fashion to TIBCO Spotfire. In addition, there are lots of products that enable you to visualise time-series data. What is rare – even to the point of almost non-existence – is to find an analytics platform that has any sort of sophisticated functions for analysing historic (stored in a database) time-series data, whether as a stand-alone function or in conjunction with real-time data. There are specialised tools that focus only on time-series data but we know of no other general-purpose tool, apart from TIBCO Spotfire, that has this ability.

The Bottom Line

TIBCO Spotfire appears to be unique. Not only have we been unable to find any product with comparable capabilities but none of the database vendors with time-series capabilities could point us in any different direction. We don’t think anything more needs to be said.