PolyAnalyst

Additional features

Access Nearly Any Data Source

Can load and merge data from diverse sources including databases (OLE DB, ODBC), Microsoft Office files, PDF, OCR, web pages, emails, XML, JSON, Facebook posts, Twitter feeds, CSV, and flat files.

Data Cleansing Operations

Includes capabilities for imputing missing values, correcting spelling errors, deduplication, handling outliers, and ensuring data quality (validity, accuracy, completeness, consistency, uniformity).

Statistical and Machine Learning Algorithms

Offers a broad selection of algorithms for tasks like classification, clustering, prediction, anomaly detection, affinity grouping, network analysis, and time series modeling (e.g., neural networks, decision trees, Bayesian networks, SVM, CHAID, logistic regression, adaptive boosting, MARS).

Natural Language Processing (NLP) Tools

Features for accurately extracting facts and relationships, performing sentiment analysis, entity recognition, document classification, clustering, keyword extraction, and handling multiple languages.

Report Creation

Creates stunning reports that summarize and communicate insights in graphical form.

Text Analytics Capabilities

Specifically designed for accurate extraction of facts and relationships, clustering, and sentiment analysis on free text documents, supporting multiple languages (over 16) and document formats.

Predictive Modeling (PolyAnalyst Pro)

Builds models that predict outcomes of future situations, training on a mixture of structured attributes and free text.

Web Reporting (PolyAnalyst Web Reports)

Delivers analytical results in simple graphical form to numerous non-technical users via a browser-based, interactive interface with 24/7 accessibility.

Customizable Web Reports

Allows preferences on color, graph type, drill-down information, company logo, and provides personalized administrative control over sensitive information.

Web Report Automation

Reports can be programmed to update at specified intervals, providing near real-time information.

Distributed Computing (PA Grid)

Supports analyzing Big Data on a cluster of low-grade machines by splitting tasks and executing them in parallel.

Intuitive Graphical User Interface (GUI)

Allows users to perform complex analysis through a visual drag-and-drop interface without writing complex code.

Data Manipulation

Provides tools for normalization, aggregation, sampling, partitioning, and derivation of new attributes.

Multi-dimensional Analysis (OLAP)

Supports building OLAP cubes for slicing data across multiple dimensions, including on free text.

Analytical Engines

Provides a variety of machine learning engines.

Reusable Analysis Scenarios

Offers visual tools for developing multi-step data analysis scenarios.

Scalability and Performance

Designed to handle huge amounts of data efficiently, with cluster implementation options for Big Data.

Client-Server System

Operates on a client-server architecture with encrypted TCP/IP communications.

Security

Features user name and password authentication, support for LDAP and MS Windows authentication, user activity logging, and HIPAA compliance.

Automated Execution

Analytical scripts can be scheduled for automated execution and reports for automated publishing.

User-Defined Dictionaries

Supports built-in, imported, and user-defined dictionaries for phrases, stop-words, synonyms, and semantic relations.

Query Syntax

Powerful query syntax for information retrieval.

Taxonomy-based Categorization

Supports categorization based on taxonomies.

Text Clustering

Performs binary and hierarchical text clustering.

Keyword and Entity Extraction

Extracts keywords, entities (over 30 standard types), facts (over 40 standard types), and relationships from text.

Sentiment Analysis

Analyzes the sentiment expressed in text.

Language Detection

Automatically detects and classifies languages.

Data Visualization

Includes various graphical outputs like histograms, scatter plots, line charts, thermal charts, rule graphs, link charts, and trend graphs.