Enterprise Report

From data preparation, style design, data calculation, data visualization to interactive logic, sharing and publishing of report development.

Provide a wealth of report resources, including grouped reports, list reports, cross-reports, paragraph reports, multi-source reports, fragmented reports and other types of reports, in order to maximize the user's choice of reports.

We also support changing the data element data (single numbers, tables, graphics) in the report template from "static" to "dynamic" in Word and PowerPoint. Whenever it is needed, data analysts can refresh these analyses like refreshing reports. Report, interpret, discuss, and suggest report data according to the input parameters, so as to spend more time on "analysis".

Summary Analysis

Ad hoc query for data services

Applicable to detailed data services under controlled permissions

  • The operation is extremely simple: check and drag fields
  • Flexible format definition: multiple headers, field redefinition, etc.
  • Support cross-database and cross-table query
  • Can be customized to extract tasks to cache
  • Security: can be controlled to rows and columns by role, organization, etc.

Perspective Analysis

Perspective analysis can combine dimensions, summarize calculations, slice, drill, and gain insights into data.

Table query: The report is directly generated through a business query, which can realize the re-analysis of the data, such as switching query parameters, changing the cross-tab/list table display mode, grouping display, aggregation, sorting, graphics, front-end filtering, etc.

Graphic analysis: Provide graphic analysis methods such as column chart, line chart, pie chart, stacked chart, dual Y axis, dashboard, etc. The graphics use HTML5 technology to dynamically display the effect.

Analysis Jump: Users can establish report links to associate multiple reports to realize jumping from one report to another. Through the analysis jump between reports, users can not only easily realize the perspective analysis of summary data to detailed data, but also can transfer parameters between related reports to realize the analysis flow.

Early warning: It can realize real-time monitoring of key information, help users find problems in time and take corresponding measures, and can set a variety of alarm conditions and alarm styles according to the scene.

Multiple output methods: Support business analysis results to be exported in TXT, CSV, HTML, PDF, Doc, Xlsx, data analysis packages and other file types.

Multiple time calculations: time calculations and secondary calculations can be set according to business attributes, such as rapid analysis of year/month/day growth rates, etc.; and support a variety of application scenarios, such as customizing the start time of the week, and fetching the same time period data Do ring comparison etc.

Visualization Dashboard

What You See Is What You Get dashboard design interface, supporting various layouts.

Abundant interactive controls and chart components, support intelligent chart recommendation.

  • Provide HTML5 dynamic charts, including dozens of dynamic interactive graphics such as waterfall chart, relationship chart, radar chart, fuel quantity chart, heat map, tree chart, etc.; support 3D dynamic graphic effects, such as 3D route chart, 3D scatter chart , 3D bar chart is used for data visualization display, and also supports the integration of other HTML5 graphic controls.
  • Map display and expansion capabilities
    Built-in typical regional maps, multiple map types, users can develop their own personalized maps according to business needs. At the same time, we also support the integration of third-party GIS system.
  • Two sets of layouts for one dashboard, multi-screen publishing to computers and mobile phones
  • The platform can also transmit analysis reports through communication software.

Machine Learning

Visual machine learning modeling

In recent years, data mining has attracted great attention in the industry. The main reason is that there is a large amount of data, which can be widely used, and there is an urgent need to convert these data into useful information and knowledge. The acquired information and knowledge can be widely used in various applications, including business management, production control, market analysis, engineering design, and scientific exploration.

Data mining is to dig out hidden, unknown relationships, patterns and trends that have potential value for decision-making from a large amount of data (including text), and use these knowledge and rules to build a model for decision support to provide predictive decision-making Supported methods, tools and processes; it is the process of using various analysis tools to discover the relationship between the model and the data in the massive data. These models and relationships can be used by companies to analyze risks and predict the future.

The purpose of data mining is to "rush for gold" from data, which is the process of obtaining value from data. Data mining provides solutions from data to value. "Machine learning" is the cornerstone of data mining, and "modeling" is the most critical link in the data mining process.

The platform has a process-oriented and visual modeling interface, built-in practical and classic statistical mining algorithms and deep learning algorithms. The simple configuration of these algorithms reduces the threshold for using machine learning and greatly saves costs. Business personnel can easily drag and drop components Perform visual modeling, complete the construction of the model process, and release and manage the model.

The platform makes the machine learning system a more general and easy-to-use platform, which can help companies easily connect related businesses to the platform, thereby helping companies use machine learning to mine and analyze corporate data and solve related business problems.

The platform gathers 50+ data mining algorithm components and flexibly builds business model processes, mainly including basic data feature processing, classification, clustering, correlation, regression, deep learning algorithms, and support for Java and Python algorithm extensions.

  • Simple operation
    The platform can easily realize the whole process of data preparation, data preprocessing, feature engineering, model training, model prediction and evaluation through intuitive and easy-to-use drag-and-drop operations, allowing users to easily use machine learning technology. In addition, users can achieve different task requirements by configuring different parameters for data preprocessing operations, feature engineering, and models.
  • Rich data preprocessing operations
    The platform supports parallel preprocessing operations, and supports a variety of data preprocessing operations, such as: merging rows, removing duplicate values, filtering null values, adding serial numbers, type conversion, random sampling, weighted sampling, hierarchical sampling, splitting Grading.
  • A large number of practical algorithms
    The platform supports a variety of efficient and practical machine learning algorithms, including classification, clustering, regression and other algorithms, including a variety of trainable models: logistic regression, progressive gradient decision trees, decision trees, random forests, and naive Bayes Persian, support vector machine, linear regression, progressive gradient regression tree, K-means, DBSCAN, Gaussian mixture model.
  • Support text analysis
    Faced with the massive amount of data in the form of text in life, we extract unknown, important, and valuable knowledge, such as word frequency analysis, analyze user preferences, help knowledge management, and improve customer service.
  • Data visualization
    The platform provides a variety of visualization effects, including data visualization, model visualization, and model evaluation result visualization.
  • Mining model self-learning
    The platform can automatically re-train with new data to ensure the accuracy of model predictions and greatly reduce the workload of operation and maintenance personnel.
  • Centralized management of models, one-click release
    The platform realizes the centralized management of the model, and is perfectly integrated with the business intelligence platform, so that the business intelligence platform has the function of data mining.

Natural Language Analysis

Intelligent interactive natural language analysis

With the widespread popularity of the Internet, the social demand for language information processing is increasing, and people urgently need to use automated means to process massive amounts of language information. Natural language processing is an important direction in the field of computer science and artificial intelligence. How to use more efficient algorithms to guide computers to process large-scale data to form an interpretable, knowledgeable, ethical, and self-learning natural language analysis system has become numerous The pursuit of AI practitioners and data analysis users.

Synapse natural language analysis is based on a pre-trained language model, giving intelligent robots powerful language analysis capabilities, which can quickly feedback corresponding information to users, and is committed to helping users perform big data analysis more intelligently to promote business development and industrial upgrading.

Users can transmit natural language through voice or text input in the system, and the platform will analyze the data analysis results of the corresponding business theme or the corresponding question and answer information to feedback to the user after analyzing, providing a more intelligent user interaction method, which greatly simplifies The process of data analysis. Users can also verify the results of the platform feedback on the front end, and provide corresponding information to the platform for automatic optimization.

In addition, the platform can also support the following functions:

  • Hot questions: The platform will provide users with the top N data of popular queries for reference
  • Intelligent diagrams: users can change the analyzed diagrams at any time through voice or self-switching
  • Analysis continuation: It supports saving the results of natural language analysis to the report platform, which can be further processed and perfected based on the analysis results to form an exquisite dashboard.