ABBYY Timeline

TimelinePI (now ABBYY Timeline) was founded in 2015 by Scott Opitz and Alex Elkin, based on their years of working with business intelligence and BPM tools. The tool focuses on the key areas of process discovery, diverse analytics, real-time robotic monitoring, and neural-network enabled prediction and alerting capabilities. Compared with the common process graph visualization of other tools, ABBYY Timeline differentiates itself through its unique Timeline visualization approach. Another noteworthy component is the Cloud-integrated ETL feature for advanced and big-data uploads with various transformation operations. TimelinePI was acquired by ABBYY in 2019.

Tool Name

ABBYY Timeline

Vendor

ABBYY (Milpitas, CA, USA)

Company Size

1001-5000 employees

Free Trial

Upon request

Licenses

Academic, Commercial

Deployment

SaaS

, On-Premises

Embedded In

–

Tested Version

4.7.3 (SaaS, Build number 277) - Tested in 04/2020

Import File Types

CSV

Supported file type can be uploaded in compressed format

Database Connections

Adapters/Connectors

Salesforce, ServiceNow, Five9, automated SFTP file loading

Integrated ETL Functionality

Data Anonymization & Pseudonymization

Data Loading

Data Refresh: Incremental data loading, appending new data to an existing set of data
Scheduled Jobs = Automatic data loading in defined time intervals

Data Refresh

, Scheduled Jobs

Character Encodings

UTF-8 compatibility tested with special characters and various languages: Korean, Japanese, Trad. & Simplif. Chinese, Hebrew, Arabic, Russian

UTF-8 (verified

), US ASCII

Attribute Types

Case-level

, Event-level

No typical case-level mapping possible, however attribute filtering can be applied to the individual process event, even if data is imported from multiple systems of record

Specify Business Hours

Working week

, Multiple shifts/day

, Exclude days

, Holiday calendar

Define Event Order

Manual definition of event ordering in case of identical timestamps. This criterion does not consider automatic ordering by the tool.

By custom sequence of activities

Start/End Timestamp

1 timestamp

» Process Graph

As-Is Process Visualization

Directly-Follows Graph (vertical), Case visualization*

*Distinctive "Timelines" visualization approach

Export As-Is Process Graph

For data exports (e.g. CSV) see “Export Reports” criterion

PNG, BPMN

Performance Highlighting

Visual highlighting of process bottlenecks

Active time

, Idle time

Process Animation (Replay)

Adjust speed

, Adjust timeframe

, Switch time mode

, Zoom in case

Search and Filter in Graph

Search and find activity names (relevant for spaghetti-like graphs)
Filter activities/transitions directly from graph

Search

, Filter

Graph Abstraction

Frequency Metrics

Activity frequency

Time Metrics

The term “duration” is used when both active and waiting/idle times can be displayed

Avg waiting time

Waiting times (not active times) can be displayed as only 1 timestamp can be loaded

Additional Graph Metrics

Cost metrics

, Custom metrics

» Process Analysis & Analytics

Process Benchmarking

Visual comparison

, Metric comparison

Process Benchmarking (Different Logs)

Visual comparison

, Metric comparison

Root Cause Analysis

Variant Breakdown by

“Duration” refers to the case throughput time

Case count, event count, avg duration, avg/total cost

Case and Activity List

Activity List

, Case List

, Case List for Variants

View Case Details

Rework Analysis

Rework can be detected through filtering

Edge/Transition Details

From-to activities: List of ingoing and outgoing activities for any selected activity

List of all transitions

, From-to activities

Compare As-Is and Target Process

Target Model Creation

In-Graph Conformance Visualization

List of Compliance Violations

Four-Eyes Principle

Sequence Filtering

“(Not) Directly followed by” filtering

Conformance Root Cause Analysis

Alert Generation

Predictive Analytics

Recommendations (Prescriptive Analytics)

Organizational Mining

Scenario Simulation

Decision Rule Mining

Export Reports

Events (CSV), Cases (

), Variants (PNG, BPMN)

Export Charts and Tables

Charts can only be exported as data (CSV)

Custom Dashboards

Custom charts

, Custom tables

Custom Metrics/KPIs

KPI Thresholds

Deviation from defined thresholds can be detected through color differentiation

Advanced Charts

Dashboard offers >5 different chart types

Chart library for multiple chart types upon request

World Map

Latitude & longitude coordinates

, Location by attribute (e.g. country codes, city names)

Save Filter Settings

UI Languages

English, Russian, Japanese

Share & Collaborate

Share selection

; Share projects, dashboards, alerts; Notes/remarks on project level

External embedding of dashboards into other systems or platforms is possible via iframe

Role-Based Access

User Authentication

Basic; 2FA (SMS); SAML 2.0; Hybrid

Hybrid = login by ID and password, while password recovery will require tokens sent by email and SMS

Audit Logs

Distinctive Focus and Features

Timeline view: The unique approach visualizes all cases in one view and provides a single detailed view for any given case. Using an event pattern analysis, sub-processes and parallel processes can be automatically detected and grouped accordingly. This method can easily handle irregular and ad-hoc processes since the history does not depend on the process nature.
Powerful filter and breakdown analysis: The Query Analysis module enables the user to easily define complex search functions based on various process variables. Besides, the Protocol module can be used to define a set of rules or procedures in a prescriptive form to filter violations. Another module worth mentioning is the Breakdown Analysis which allows to easily drill down activities to attributes and visualize them in a treemap chart.
Forecasting: ABBYY Timeline offers an elaborate out-of-the-box forecasting functionality. The user can build a forecasting model with any desired process outcome for newly added data (open cases) and define significant attributes that are considered to have strong impact on the outcome. The system is trained based on historical data and returns a confusion matrix with false positives and false negatives. In addition, forecast outcomes can be filtered to trigger alerts depending on a previously defined confidence threshold. Notifications can be sent via SMS, email or directly to an IT system through a webhook.

Explore ABBYY Timeline

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Disclaimer: The timeliness of provided information is based on the tested version and date as stated under “Tested Version”. No guarantee can be given about the correctness and accuracy of the information contained.

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