Data Exploration. Quick Start. Service Application. Configuring Analysis System Parameters. Operation Process

Transcription

1 Data Exploration Quick Start Operation Process The data exploration includes self-service data obtaining and exploratory data analysis. Self-service data obtaining can obtain data sets from service features or service models. Exploration and analysis are performed after data set obtaining. Application release and sharing are implemented finally. Service Application Configuring Analysis System Parameters To use the exploration and analysis function, configure the tenant cluster information first and assign Hadoop cluster permissions to the exploration and analysis tenant.

2 Prerequisites The Hadoop cluster has been created and Hadoop resources have been created. The involved tenant has been created and Hadoop resources have been allocated to the tenant. Procedure Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose System > System Maintenance > ISAE System Parameter Setting. The System Configuration page is displayed. Step 3 Click Edit next to the code ISAE_TEAMID. The Data column becomes available. Step 4 Select a tenant in Data and click Save. ----End Self-Service Data Obtaining Self-service data obtaining is a process of extracting data from data sources. Data extracted by self-service data obtaining can be directly downloaded and can be used for self-service analysis and application development during exploratory data analysis. (Optional) mporting DataI Prerequisites Context If the UDGP does not contain data models requiring data extraction and related data files have been obtained, you can use the data import function to generate data models and then use the data models to extract data. A tenant has been configured for the data import user and the tenant has the permission to import data. Data to be imported must be stored based on the following requirements: Stored in specified format: The.txt and.csv formats are supported. Stored in specified encoding format: The UTF-8 and GBK formats are supported. Each row contains only one record. Use the specified separator to separate attribute values. The following characters can be used as delimiters: comma (,), vertical bar ( ), Start Of Headline (character whose ASCII code is x01), and horizontal tab. The first row can be the table title that contains column names separated by separators. After data is imported, you can view the imported data models in the data model tree during data obtaining of the data model, implementing the data obtaining operation on the imported data.

3 Procedure Log in to the Universe Portal and choose Intelligent Analysis. Step 1 Choose My Data. Step 2 Click Create Data > Obtain from file > Import Data Model. The Import Data page is displayed. Step 3 Configure the file source. Select the file source. If HDFS File is selected, files can be obtained from an HDFS directory and data can be extracted. Click, select the data file stored in specified format, and click Open. Select the delimiter. If the first row is the table header, set First line as table header to Yes. Step 4 Configure the database where the target data is stored. Accelerate Database If this database is selected, the system determines the tenant of the logged-in employee and then the cluster, Hadoop user, and database based on the mapping between tenants and acceleration databases configured in the tenant cluster information. Other Database If this database is selected, the system determines the tenant of the logged-in employee. The employee can select a cluster, Hadoop user, and database of the tenant. Step 5 Enter a model name in Model Name. Step 6 Click Parse Format. The system automatically identifies the column name, type, length, and precision in the data file, as shown in Figure 1-1.

4 Figure 1-1 Setting the model format Step 7 Modify Column Name, Column Type, Length, and Precision based on the site requirements and select rows to be used as columns. The field length cannot be less than the actual data length. Otherwise, the preview fails during data obtaining based on the data model. Step 8 Click Upload data and create a Model.. After the import is successful, imported data models are displayed on the My Data page. ----End Extracting Data Sets from Service Features Prerequisites Procedure This topic describes how to extract data by setting filter rules in features configured in the DG. Service features have been configured in the DG and synchronized to the ISAE. The synchronization takes about 10 minutes. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose My Data. Step 3 Click Create Data > Obtain from database > Business Features. Step 4 Click All in the directory tree on the left, select corresponding features under the feature domain and subdomain, and drag the selected features to the filter criterion area on the right, as shown in Figure 1-2.

5 Figure 1-2 Selecting features On the All tab page, you can enter a keyword and search for and obtain the required features. You can click Hot or My Mostly Used and select hot features or common features to extract data. Step 5 Configure filter rules. Click a feature and configure the default calculation or historical period calculation for the feature. The ISAE supports flexible comparison calculation based on historical period data. Configure the filter rule for the feature, for example, "is greater than 0." Click Group to combine features. You can set the combination relationship to Or or And. Step 6 Configure output columns. 1. Select features to be used as fields in the directory tree on the left and drag the selected features to the output column area on the right, as shown in Figure 1-3as_opg_00007_mMcCpPsS_fig_02. Figure 1-3 Selecting output columns

6 2. Click in the output column to modify the output column name or configure multi-period value calculation. 3. (Optional) Click Computed to add calculation fields based on calculation functions. Calculation functions supported by the output column are similar to those of data model-based data obtaining. For details, see Click Choose Data Of Data and select multiple period for data obtaining calculation in the dialog box that is displayed. Step 7 Click Preview and check whether the data meets the extraction requirements. Step 8 Click Save Data Set, set Data Set Name and Data Set Description, and click OK. After you click OK, the ISAE starts extracting data based on the configured filter rules and output columns. If the data amount is huge, the ISAE automatically extracts the data in the background. Generated data sets are displayed on the My Data page. Figure 1-4 shows that the data extraction is completed. Figure 1-4 Data extraction completed Step 9 Click a generated data set to preview the data set details and data distribution information. On the Data Distribution tab page, you can click icons in the Data Distribution Map column of a feature and view data distribution details of the feature, as shown in Figure 1-5.

7 Figure 1-5 Viewing data distribution ----End Extracting Data Sets from Data Models Prerequisites Procedure This topic describes how to extract data sets in advanced mode by setting output columns, filter criteria, and calculation expressions in data models configured in the DG. Data models have been configured in the DG and synchronized to the ISAE or data models and data have been obtained by importing data, the synchronization takes about 10 minutes. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose My Data. Step 3 Click Create Data > Obtain from database > Data Connection. Step 4 Select entities under the data model in the directory tree on the left and drag the selected entities to the Model Data area on the right, as shown in Figure 1-6. Figure 1-6 Selecting entities

8 You can enter a keyword on the All tab page to search for entities or attributes. The entity names and attribute names matching the keyword are displayed in the search result. In the search result, you can: Click Show Attribute to view the list of matching entity attributes related to the keyword. Click next to an entity to view the matching results of fields in the entity. You can click Hot or My Mostly Used and select hot entities or common entities to extract data. You can also click My data and select data models based on the imported data to extract data. Step 5 (Optional) Configure the association relationship. When entities need to be associated, you need to configure the association relationship between the entities. After you drag entities to the workspace, the system matches fields by default and automatically associates entities. If the default association relationship does not comply with the service scenario, you need to reconfigure the association relationship. 1. Click between entities. If two entities are not automatically associated, you need to move the mouse pointer to one entity and drag the displayed line to the other entity to associate the two entities. Configure the relationship type (for example, Left) in the dialog box that is displayed. By default, the ISAE provides four types of association relationships: Internal, Left, Right, and External. (Optional) Click to delete a redundant association relationship. If the default association relationship is not required for the service scenario or the matching is incorrect, you need to delete the association relationship. (Optional) Click drop-down list boxes on both sides of =, select association fields, and configure the association relationship. If the default association relationship does not meet the service scenario requirement, you need to add an association relationship. Figure 1-7shows the configuration result.

9 Figure 1-7 Configuring the association relationship If the association relationship between two fields requires conversion, click and configure a calculation function. The function is a common function in the database. For details about the supported functions, see the 0. Click at the upper right corner of the association relationship dialog box or click the blank area outside the dialog box to return to the workspace. Step 6 Add output columns and filter criteria. Output columns and filter criteria are selected entity fields. You can select an entity and add output columns and filter criteria based on the site requirements. 1. Click an entity in the workspace. Details of all fields in the entity are displayed at the lower part of the workspace. Click the blank area. Fields of all entities are displayed at the lower part of the workspace by default. Select output columns and click Add Output Column to add output columns, as shown in Figure 1-8.

10 Figure 1-8 Adding output columns After being added successfully, the output columns are displayed on the Output Column tab page. Select output columns and click Add Filter to add filter criteria. After being added successfully, the filter criteria are displayed on the Filter Criteria tab page. Step 7 Configure filter criteria. Filter criteria are dimensions for extracting data and analyzing data in exploration mode. You can configure filter criteria to extract data meeting the specified conditions. 1. Click the Filter Criteria tab. By default, the relationship between filter criteria is And. (Optional) Click and add advanced filter criteria. Calculation conditions need to be added for filter criteria requiring calculation, for example, gprs_m. Filter criteria support only line-level calculation. Enter a calculation formula name, set the calculation result type, select a calculation function, select fields in the function, and click OK after ensuring that the calculation result is correct, as shown in Figure 1-9.

11 Figure 1-9 Configuring calculation conditions The function is a common function in the database. For details about the supported functions, see the Functions Supported in Calculation Formulas. After being added successfully, the calculation conditions are displayed on the Filter Criteria tab page. Set Relationship and Value for the fields of the added filter criteria, as shown in Figure Figure 1-10 Configuring filter criteria The filter criteria supports the following relationships: Character string filtering: equal to, not equal to, greater than or equal to, less than, less than or equal to, between, belong to, not belong to, empty, non-empty, include words, not include words, match prefixes, match suffixes. Number and date filtering: equal to, not equal to, greater than or equal to, less than, less than or equal to, between, belong to, not belong to, empty, and non-empty. The statistics date and month can be selected on the date control.

12 The filter criteria value supports the following: Periodical generation: The format is ${Yesterday} and ${Last Month}, indicating that the filter criteria for periodically generating data sets are set to the previous day or previous month. If a field is associated with the dimension table and set as a filter criterion, you can select a dimension during filter criterion setting and do not need to manually enter the dimension value. Calculation fields serve as filter criteria. Subquery filtering: This is valid only for the Belong to and Not belong to relationship. Click Show Advanced, select conditions, click Combination, and configure the combination relationship. You can configure the advanced relationship, as shown in Figure Figure 1-11 Advanced conditions Step 1 Configure output columns. 1. Click the Output Column tab. Double-click Attribute in the output column and change the attribute name. Double-click the statistics method of the corresponding field in the output column and select a statistics function, as shown in Figure If the statistics function is used, all dimensions are used as the benchmark for aggregation calculation. Figure 1-12 Setting the statistics method (Optional) Click, add the calculation output column, set the calculation formula, and click OK after ensuring that the formula result is

13 correct, as shown in Figure You can perform this step to define a calculation method when simple statistics functions cannot meet the calculation requirements. Figure 1-13 Defining a calculation method (Optional) Select the Data Set Deduplication check box if the output data needs to be deduplicated. Step 8 Click the Sample Data Preview tab and click Preview to preview data. On the Sample Data Preview tab page, you can click to change the column name, set statistics method, or remove the column and click Use Sample Data to Preview to preview sample data. The data preview is fast but has errors. Step 9 Click Generate Data, set Data Set Name and Data Set Description, and click OK. After you click OK, the ISAE starts extracting data based on the configured filter criteria and output columns. If the data amount is large, the ISAE automatically extracts data in the background and backs to the home page. The generated data set information is displayed on the home page. Figure 1-14 shows that the data extraction is completed.

14 Figure 1-14 Data extraction completed You can further associate, filter, and export field design based on the extracted data sets to extract new data sets. Step 10 Click a generated data set to preview the data set details and data distribution information. On the Data Distribution tab page, you can click icons in the Data Distribution Map column of a field and view data distribution details of the field, as shown in Figure Figure 1-15 Viewing data distribution ----End Data Set-based Secondary Data Obtaining Users can associate fields, filter fields, export field design, and calculate the union set, difference set, and intersection set of extracted data sets to obtain new data sets.

15 Prerequisites Procedure Data has been successfully obtained from service features or data models. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose My Data. The My Data page is displayed. Step 3 Select a data set and click to go to the data set editing page. Step 4 Perform data set association and filtering and design the output fields. Step 5 Click Data Set Operations. Step 6 The data set editing page is displayed. Step 7 Select My Data Set in the navigation tree on the left and drag data sets involved in calculation to the editing area on the left. Step 8 Select data sets, perform the Union, Intersect, and Except operations, and configure set calculation conditions. Step 9 Click Generate Data. ----End Sharing a Data Set Prerequisites Context Procedure Data sets generated during self-service data obtaining can be shared to other users and roles. Users and roles to which the data sets are shared can access the data sets and explore and analyze the data sets. Data has been successfully obtained from service features or data models. When creating a user, you need to assign a role to the user to control the user's rights. For details about users and roles, see the operation guide of the unified portal. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose My Data > My DataSet. Step 3 Click and click from the pop-up icons. Step 4 In the Shared Data Set dialog box, click User or Role tab, select users or roles to which the data set is shared, and click OK. Step 5 Log in to the Universe as a user or a user with the role to which applications are shared, go to the My Data page, and view, download, or further explore and analyze the shared data set.

16 ----End Downloading a Data Set Prerequisites Procedure Data sets generated during self-service data obtaining can be downloaded and saved to a local disk as TXT and CSV files, facilitating viewing and further analysis. Data has been successfully obtained from service features or data models. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose My Data. Step 3 Click in the data set in the My Data area and click from the pop-up icons. Step 4 In the Select Download Type dialog box, select the TXT or CSV format, configure the local disk path and file name, and click OK. Step 5 Open the downloaded data set in the local disk to view details about the data set. ----End Importing and Exporting Data Sets Exporting Data Sets Importing Data Sets Data set export is equivalent of exporting information including the search criteria and output fields configured in the data model or feature. Log in to the Universe Portal and choose Intelligent Analysis. Choose My Data. Select the required data sets, click at the upper right corner, and click Export. The Export page is displayed. Confirm data to be exported and click Export. Click at the upper right corner of the page and click Import. The Import page is displayed. Click and select a data file in ZIP format from the local host. Choose Whether to Cover and click OK. Exploratory Data Analysis You can configure different charts to analyze, compare, and display extracted data, including analyzing the data composition, trend, and distribution and displaying the analysis result on the page.

17 Creating an Exploration Control Prerequisites Context Exploratory data analysis provides common data analysis templates to implement quick and convenient data exploration. Self-service data obtaining has been completed to generate data sets. The ISAE automatically recommends applicable charts for exploration based on the selected dimensions and measurements and implements one-click switch between charts. Table 1-1 describes the automatic chart recommendation rule of exploratory data analysis. Table 1-1 Automatic chart recommendation rule of exploratory data analysis Number of Dimensions Number of Measurements Recommended Chart Charts Supporting One-Click Switch 1 1 Bar Chart Pie Chart >=2 Line Chart Column Chart, Area Chart, Scatter Chart 2 1 Stacking Column Chart Stacking Bar Chart, Rectangle Tree 2 Rectangle Tree Scatter Chart 3 Rectangle Tree Bubble chart 4 Rectangle Tree Bubble chart. The color shade indicates the last measurement. >=5 Rectangle Tree - >=3 1 Rectangle Tree - 2 Rectangle Tree Scatter Chart 3 Rectangle Tree Bubble chart 4 Rectangle Tree Bubble chart. The color shade indicates the last measurement. >=5 Rectangle Tree - For details about charts for analyzing and displaying data in the ISAE, see the 0Appendix.

18 Procedure Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose Exploration Analysis. Step 3 Click Create Analysis, select data sets to be analyzed on the Select Data Set page, and click Analysis and Exploration. Step 4 Select data to be analyzed. 1. In the navigation area on the left, select dimensions, hierarchies, measurements, and geography to be analyzed in the data sets and drag them to the workspace on the right, as shown in Figure Figure 1-16 Selecting data You can enter a keyword in the navigation area on the left to search for dimensions or measurements. If multiple data sets are selected for analysis, you can click the data set name at the upper part of the navigation area on the left and select a data set from the drop-down list box. Automatically recommended charts are displayed in the workspace. If two dimensions and three measurements are selected in Figure 1-16, the rectangle tree chart is automatically recommended by default. Other recommended analysis charts are displayed at the lower part of the workspace. You can click to show or hide recommended charts or click a recommended chart to show it. Right-click each added measurement and set the ranking and aggregation mode of each measurement, as shown in Figure 1-17.

19 Figure 1-17 Setting the measurement ranking and aggregation mode After setting the measurement aggregation mode, you can perform the Sum, Count, Distinct Count, Average Value, Min. Value, and Max. Value calculation on data in the data sets. Step 5 Configure filter criteria. 1. Click at the upper left corner of the navigation area on the left to open the filter panel. Select dimensions or measurements on the left and drag them to the filter panel. Configure filter criteria based on the automatically generated filter rules. For example, you can filter dates based on the date dimension, as shown in Figure Figure 1-18 Configuring filter criteria After filter criteria are configured, the filtered data analysis result is displayed in the workspace on the right.

20 You can click to close the filter panel. Step 6 Switch to different charts to analyze and explore data. You can click an icon at the upper left corner of the workspace to switch to the corresponding chart at one click. For example, you can click to switch to the scatter chart or bubble chart, as shown in Figure Figure 1-19 Switching the chart at one click For details about the icons at the upper right corner, see Icons and Controls Supported by Exploratory Data Analysis. Step 7 (Optional) If recommended charts cannot meet the analysis requirements, click open the tool box and select a proper chart to explore data. to You can drag dimensions and measurements to configure filter criteria and measurement ranking and aggregation mode. Then, you can click and set chart attributes to generate the exploratory data analysis chart. For details about the procedure, see Creating an Analysis Control. Step 8 Click Save, set Analysis Theme Name and Analysis Theme Description, and save the exploratory data analysis result. After the configuration is successfully saved, the exploratory data analysis result is displayed on the Exploration Analysis page. The exploratory data analysis result can be directly released to the application market or be used for further analysis. ----End Creating an Analysis Control You can select the analysis and display tool based on the site requirements and analyze data sets by dragging.

21 Prerequisites Context Self-service data obtaining has been completed to generate data sets. For details about charts for analyzing and displaying data in the ISAE, see the Appendix Procedure. For details about controls, see Icons and Controls Supported by Exploratory Data Analysis. The ISAE supports further analysis based on the newly created exploration control. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose Exploration Analysis. Step 3 Click Create Analysis, select data sets to be analyzed on the Select Data Set page, and click Analysis and Exploration. The exploration page is displayed by default. You can follow instructions in 0to explore and save data first. You can click and drag the created exploration control to the workspace for further analysis in the created analysis control. Step 4 Click to create a dashboard. Step 5 (Optional) Click and drag completed exploratory data analysis result to the workspace. Exploratory data analysis completed in 0can be directly dragged to the workspace for further analysis. Step 6 Click in the navigation area on the left to go to the tool box page and drag required tools to the workspace on the right. You can click at the upper left corner to set the layout. You can click other icons to set the alignment mode and background color. Generally, the default layout and settings are used. Figure 1-20 shows tools supported by the ISAE.

22 Figure 1-20 Analysis and display tools Step 7 Set basic and advanced tool attributes. 1. Select the tool to be set, click, and set basic tool attributes, for example, the name, width, and height of the chart. Click Advanced in the basic attribute area or click and set advanced tool attributes, for example, Chart Type. Step 8 Select dimension, hierarchy, measurement, or geography data.

23 1. Click to go to the model selection page. Drag the dimension, hierarchy, measurement, or geography data to the corresponding tool based on the tool function, as shown in Figure Figure 1-21 Selecting data to be analyzed Set filter criterion attributes. Take the time control as an example. After you drag stat_date to the workspace, information shown in Figure 1-22 is displayed. Figure 1-22 Setting time attributes Click and click Set the aggregation mode on the advanced attribute tab page to set the measurement aggregation function. After setting the measurement aggregation mode, you can perform the Sum, Count, Distinct Count, Average Value, Min. Value, and Max. Value calculation on data in the data sets. Click Set the sort mode on the advanced attribute tab page to set the measurement sorting mode.

24 You can sort measurements in ascending order or descending order or do not sort measurements. Step 9 Click Preview to view the dashboard page and effect. Step 10 Click Save to save the exploratory data analysis result. After the configuration is successfully saved, the exploratory data analysis result is displayed in the My Analysis area on the home page. ----End Importing and Exporting Analysis Results Data set-based analysis templates can be exported and imported to obtain the exploratory analysis result. Exporting Exploratory Analysis Log in to the Universe Portal and choose Intelligent Analysis. Choose Exploration Analysis. The My Analysis page is displayed. Select the required analysis, click at the upper right corner, and click Export. The Export page is displayed. Confirm data to be exported and click Export. Importing Exploratory Analysis Click at the upper right corner of the page and click Import. The Import page is displayed. Click and select a data file in ZIP format from the local host. Choose Whether to Cover and click OK. Using the Qlik Sense for Exploration and Analysis Prerequisites Context Procedure The QlikSense can be integrated on the GUI of the ISAE to replace the exploration and analysis function of the ISAE. The ISAE provides the QlikSense entrance for operators. The ISAE and Qlik Sense have been connected. The exploration and analysis function of the ISAE and the Qlik Sense are incompatible. After the Qlik Sense is integrated, the Qlik Sense replaces the exploration and analysis function of the ISAE. Step 1 Log in to the Universe Portal and choose Intelligent Analysis. Step 2 Choose Exploration Analysis.

25 Step 3 Click Create Analysis, select data sets to be analyzed on the Select Data Set page, and click Analysis and Exploration. Step 4 The Qlik Sense page is displayed. Step 5 Create exploration and analysis by following instructions in the QlikSense Help. ----End Application Release and Sharing Releasing an Application Prerequisites Procedure You can release the exploratory data analysis result as an application and view the application details in the application market. Applications in the application market can be shared to other users and roles. After exploratory data analysis, you can release the fixed analysis result to the application market. The application market provides a unified interface for accessing the application and viewing the analysis result. The analysis result has been created on the exploratory data analysis page. Step 1 Log in to the Universe Portal as an application development user. Step 2 Click Intelligent Analysis and click Exploration Analysis on the Intelligent Analysis page that is displayed. Step 3 In the My Analysis area, click above the application to be released. If the configured time is not a periodical time, the message Periodical update is not enabled for this application. The data will remain unchanged. Are you sure you want to release this application? is displayed. Click Yes in the message dialog box to release the application. If the application date needs to be updated periodically, you need to configure the periodical update during self-service data obtaining. Step 4 After the application is released, click Application Market and view information about the released application in the application market. Figure 1-23 shows information about the released application.

26 Figure 1-23 Information about the released application Step 5 Click the application to view details about the application. Step 6 Select the required application and click ----End at the upper right corner to export it. Sharing an Application Prerequisites Context Procedure In the application market, application developers can share the developed application to other users and roles so that other users and roles can view and access the application. The application has been developed and released. When creating a user, you need to assign a role to the user to control the user's rights. For details about users and roles, see the operation guide of the unified portal. Step 1 Log in to the Universe Portal as an application development user. Step 2 Click Intelligent Analysis and click Application Market > Analysis Application on the Intelligent Analysis page that is displayed, and go to the My Application page. Step 3 Click above the application to be shared. Step 4 In the Share Application dialog box, click User or Role tab, select users or roles to which the application is shared, and click OK. Step 5 Log in to the Universe as a user with a role to which the application is shared, go to the My Application page, and view details about the shared application. ----End

27 Appendix Charts Supported by Exploratory Data Analysis During exploratory data analysis, you can use different chart controls to analyze data. Table 1-2 describes the supported charts. Table 1-2 Charts supported by exploratory data analysis Chart Type Example Application Scenario Description Column chart Stacking column chart This chart is used for analyzing trends and comparing development information of different members in different periods, for example, analyzing the revenue trend of a product from January to December in a year. The column chart displays data change in a time period or comparison information between items. Recommended analysis scenario: one dimension field and two to N measurement fields This chart is used for displaying the relationship between a single item and the overall information and comparing the proportion of each category value to the total value. The stacking column chart displays values in two-dimensional vertical stacking rectangles. If multiple data series exist and you want to emphasize the total value, you can use the stacking column chart. Recommended analysis scenario: two dimension fields and one measurement field Use the dimension field as the X axis and measurement fields as values. If the dimension field has a statistics period, use the statistics period field as the X axis. Use one dimension field as the X axis, the other dimension field as the series, and measurement field as the value. If the dimension field has a statistics period, use the statistics period field as the X axis.

28 Chart Type Example Application Scenario Description Bar chart Stacking bar chart Line chart Similar to the column chart, this chart is used for analyzing trends. When a large number of dimension values (more than 1000) exists, the bar chart is used for display in priority. Recommended analysis scenario: one dimension field and one measurement field Similar to the stacking bar chart, this chart is used for comparing the proportion of each category value to the total value. Recommended analysis scenario: two dimension fields and one measurement field This chart is used for analyzing trends in comparison mode, for example, comparing the revenue trends of different products from January to December in a year. The line chart can display consecutive data changing with the time (based on the common ratio setting) and is suitable for displaying data trends in equal durations. In the line chart, the category data is evenly distributed along with the horizontal axis and all values are evenly distributed along Use the dimension field as the Y axis and measurement field as the value. If the dimension field has a statistics period, use the statistics period field as the X axis. Use one dimension field as the Y axis, the other dimension field as the series, and measurement field as the value. If the dimension field has a statistics period, use the statistics period field as the Y axis. Use the dimension field as the X axis and measurement fields as the Y axis. The currently displayed chart is a column chart or bar chart and has two dimensions and one measurement. You can convert the chart into a line chart. If the smaller dimension

29 Chart Type Example Application Scenario Description with the vertical axis. Recommended analysis scenario: one dimension field and one to N measurement fields value is less than or equal to 10, the system automaticall y uses the dimension with more dimension values as the X axis and others as the curve and provides a legend. If the smaller dimension value is greater than 10, the chart cannot be converted into a line chart. Pie chart This chart is used for analyzing composition and comparing the ratio of each member, for example, analyzing the ratio of each product's revenue to the total revenue. The pie chart displays the ratio of each product to the total. Values displayed in the pie chart indicate the percentage of corresponding items in the pie chart. The pie chart is used in the following scenarios: There is only one data series. There is no negative value. There is nearly no zero value. Use the dimension field as the X axis and measurement field as the Y axis. The system automatically merges dimensions whose values are small and displays them as one percentage. Users can manually merge some dimensions.

30 Chart Type Example Application Scenario Description The number of categories is no more than seven. Each category represents a part of the pie chart. Recommended analysis scenario: one dimension field and one measurement field Scatter chart Bubble chart This chart is used for displaying the relationship between values of several data series or displaying two groups of data as a chart with the X and Y axises. The scatter chart has two value axises: X axis for displaying a group of data and Y axis for displaying another group of data. The scatter chart merges data into single data points and displays them unevenly. Generally, the scatter chart is used for displaying and comparing data, for example, science data, statistics data, and project data. Recommended analysis scenario: one to N dimension fields and two measurement fields This chart uses a third measurement field compared with the scatter chart. A larger bubble indicates a greater measurement value. Recommended analysis scenario: one to N dimension fields and three to four measurement fields Use the dimension fields as the X axis and measurement fields as the Y axis. Use the dimension fields as the X axis and measurement fields as the Y axis. The last measurement field determines the bubble size.

31 Chart Type Example Application Scenario Description Area chart Cross table This chart is used for analyzing trends in comparison mode, for example, comparing the revenue trends of different products from January to December in a year. The area chart emphasizes the degree that the quantity changes with time and can call attention to the total value trend. For example, profit data that changes with time can be displayed in an area chart to emphasize the total profit. By displaying the total value, the area chart can also display the relationship between a part and the total. Recommended analysis scenario: one dimension field and one to N measurement fields This table is a common categorized summary table. It is a basic report with groups at both the row and column directions. Recommended analysis scenario: one to N dimension fields and one to N measurement fields Use the dimension field as the X axis and measurement fields as the Y axis. Drag the row and column dimensions to the corresponding areas and drag the measurement fields to the data area.

32 Chart Type Example Application Scenario Description Rectangle tree chart Planar table This chart implements a hierarchical and visual chart structure that can clearly display data proportions. Recommended analysis scenario: two to N dimension fields and one measurement field (The dimension fields can be hierarchical dimensions.) This table is used for displaying details about tables stored in the database. Recommended analysis scenario: displaying details (for example, drill-down data) in a table Use the dimension fields as the X axis and measurement field as the Y axis. Directly drag the dimension field and measurement field into the workspace. The map control supports HTTPS access to Baidu Map. To use the Baidu Map service, carriers need to purchase the service themselves. Icons and Controls Supported by Exploratory Data Analysis Creating an Exploration Control Table 1-3 describes icons for creating an exploration control. Table 1-3 Icons for creating an exploration control Icon Description Switches to the column chart at one click. Switches to the bar chart at one click. Switches to the line chart at one click. Switches to the pie chart at one click. Switches to the stacking column chart at one click.

33 Icon Description Switches to the stacking bar chart at one click. Switches to the scatter chart or bubble chart at one click. Switches to the area chart at one click. Switches to the rectangle tree chart at one click. Displays data in the data view. Saves the chart analysis result as a picture that can be downloaded to a local disk. Opens or closes the setting page of the dimension and KPI. After the page is opened, you can drag dimensions and KPIs to the page. After the page is closed, the chart can be completely displayed. Restores the previous operation. Deletes all dimensions and KPIs to restore to the initial state. Creating an Analysis Control When an analysis control is created, different controls can be used for data analysis. Table 1-4describes the supported controls. Table 1-4 Supported controls Control Example Application Scenario Description RadioButton CheckBox ComboBox TextBox Date Data of each KPI needs to be viewed based on a member. Data of each KPI needs to be viewed based on multiple members. Data of each KPI needs to be viewed based on a member. Text or numbers need to be entered in a control and associated with charts. Generally, the text box needs to be used with the label. Data of each KPI needs to be viewed based on the time dimension. Directly drag a dimension to the option button. Directly drag dimensions to the check box button. Directly drag a dimension to the drop-down list box. Directly drag a dimension to the text box. 1. Drag the Date control to the design area. 2. Select the Date

34 Control Example Application Scenario Description DateRange Data of each KPI needs to be viewed based on a time range. control and click. 3. Configure information such as the date format. For example, if the date is accurate to the month, set the date in the yyy-mm format. Similar to that of the Date control. BlankPanel Tab You can use the BlankPanel control to combine controls and control the positions of all controls in the design area. Multiple related themes need to be analyzed in the same design area. You can easily view the display results of different themes by switching tab pages. Drag the BlankPanel control to the design area and drag other controls to the BlankPanel control. 1. Drag the Tab control to the design area. 2. Select the Tab control and click. 3. Click Add Panel, increase the number of tabs, set other attributes, and configure information including the tab length and font. 4. Select different tabs of the Tab control and add other controls to the design area of each tab based on the site requirements. On the Advanced tab page, you can select the Lock check box next to a control to lock the control. After the control is locked, you cannot drag controls to the control but can drag controls out of the control. After you select the Enable Alternating

35 Control Example Application Scenario Description Play check box, the system displays all tab pages in the window by turns. This parameter is valid only when the number of tab pages is greater than two. DataLabel TextLabel Image Connection Line A number needs to be displayed. Generally, this control is used with other controls. A piece of text needs to be displayed. Generally, this control is used for displaying information on the GUI. An image needs to be added to the design area. A connection line needs to be added to connect controls or separate the area. This control is 1. Drag the DataLabel control to the design area. 2. Drag the dimension or measurement of a number to the control. 3. Select the DataLabel control and click. 4. Click Set the aggregation mode, set the number statistics method (for example, SUM), and click OK. Drag the control to the design area and enter information in the text box. You can double-click the control to modify the information. 1. Drag the Image control to the design area. 2. Select the Image control and click. 3. Enter the image path information in the Image Address text box. The image path is a path relative to the installation server. You need to upload the image to the server first. 1. Drag the ConnectionLine control to the design

36 Control Example Application Scenario Description used only for displaying a line. area. 2. Select the ConnectionLine control, click, and configure the length, color, and type of the connection line. Functions Supported in Calculation Formulas During self-service data obtaining, different calculation formulas are supported for setting filter criteria and output columns. Table 1-5 describes functions supported in calculation formulas. Table 1-5 Functions supported in calculation formulas Type Function Format Description Number ABS ABS(Numerical expression) Return the absolute value of the numerical expression. CEIL FLOOR MOD ROUND SIGN CEIL(Numerical expression) FLOOR(Numerical expression) MOD(Integer expression 1,Integer expression 2) ROUND(Number, Position) SIGN(Numerical expression) Return the smallest integer that is greater than or equal to the value of the numerical expression. Return the largest integer that is less than or equal to the value of the numerical expression. Return the remainder after dividing the value of integer expression 1 by the value of integer expression 2. Integer expression 2 cannot be 0. Round off a value. Return the sign of the value of the numerical expression. If the value of the numerical expression is positive, the function returns +1. If the value of the numerical expression is 0, the function returns 0. If the value of the numerical expression is negative, the function returns -1.

37 Type Function Format Description Character String Character 1 Character 2 Concatenate or combine the two character strings. ASCII ASCII (Character) Obtain the ASCII code of the input character. GeoMakePoi nt CONCAT LENGTH LOWER LPAD LTRIM REPLACE RPAD GeoMakePoint(Longi tude,latitude) CONCAT(Character string1,character string 2) LENGTH(Character string) LOWER(Character string) LPAD(String,Length, Character) LTRIM(Character expression 1[,Character expression 2]) REPLACE(Character String, Character 1,Character 2) RPAD(String,Length, Character) Generate hot coordinates in maps. This function is used for developing map apps. For example, GeoMakePoint(' ',' ') returns ' '. Concatenate character string 1 and character string 2 to generate a new string. For example, CONCAT(Area code (020),Phone number (8 digits)) returns Area code (020) Phone number (8 digits). Calculate the length of the input character string. Convert the input character string to a character string in lowercase. Pad the left-side of the input string with the specified characters based on the specified length. Remove character expression 2 and characters preceding to character expression 2 from character expression 1. If character expression 2 is not specified, remove all leading space characters from character expression 1. For example, LTRIM('ABxXxyx','AB') returns 'xxxyx'. Replace character 1 in the input string with character 2. Pad the right-side of the input string with the specified characters based on the specified length.

38 Type Function Format Description RTRIM SUBSTRING TRANSLAT E TRIM TRUNC UPPER RTRIM(Character expression 1[,Character expression 2]) SUBSTR(Character string A,Location B,[Substring length C]) TRANSLATE(Charac ter string, Character string 1,Character string 2) TRIM(Character string) TRUNC(Number 1,Number 2) UPPER(Character string) Remove character expression 2 and characters following character expression 2 from character expression 1. If character expression 2 is not specified, remove all space characters from the right-hand side of character expression 1. For example, RTRIM('ABxXxyx','xy') returns 'ABxX'. Return the substring of length C from location B in character string A. If C is not specified, this function returns the substring starting from B. For example, SUBSTRING(' ', 6,2) returns '02'. Replace character string 1 in the input string with character string 2. Remove leading space characters at the beginning and end of the input string. Truncate the number 1 based on number 2. Convert the input character string to a character string in uppercase. Date and Time HOUR HOUR(Time) Extract the hour from time. For example, if the user registration time is :45:33, HOUR(User registration time) returns 11. MINUTE MINUTE(Time) Extract the minute from time. For example, if the user registration time is :45:33, MINUTE(User registration time) returns 45. SECOND SECOND(Time) Extract the second from time. For example, if the user registration time is :45:33, SECOND(User registration time) returns 33.

39 Type Function Format Description YEAR YEAR(Time) Extract the year (4-digit) from time. For example, if the user registration time is :45:33, YEAR(User registration time) returns MONTH MONTH(Time) Extract the month from time. For example, if the user registration time is :45:33, MONTH(User registration time) returns 1. DAY DAY(Time) Extract the day from time. For example, if the user registration time is :45:33, DAY(User registration time) returns 6. Format TO_CHAR TO_CHAR(Data A,Format) Convert the input data to a character string based on the format. For example, TO_CHAR(123, '90.00') returns TO_NUMBE R TO_DATE TO_NUMBER(Chara cter string,format) TO_DATE(Character string, Format) Convert the input character string to a number based on the format. For example, TO_Number(' ', '99') returns 12. Convert the character string to a date. For example, TO_DATE(' :49:18', 'YYYY-MM-DD HH24:MI:SS') returns :49:18. Aggregation SUM SUM(Number) Calculate the sum of all numbers. AVG AVG(Number) Calculate the average value of all numbers. COUNT COUNT DISTINCT Count([DISTINCT] Data) COUNT([DISTINCT] Data) Calculate the number of data occurrences. The optional parameter DISTINCT indicates counting the occurrences of unique data. Calculate the number of the occurrences of unique data. MAX MAX(Data) Calculate the maximum value of all data. MIN MIN(Data) Calculate the minimum value of all data.

40 Type Function Format Description Sentence Pattern and Structure CASE CASE WHEN Condition expression 1 THEN Branch 1 WHEN... ELSE Other branch expressions END Case search function. This function returns the branch whose condition expression is firstly met. Simple CASE function CASE Expression 1 WHEN 'A' THEN A expression ELSE Other expressions END Simple case function. This function returns only the result that firstly meets expression 1. Operator + Number A + Number B - Number A - Number B * Number A x Number B Calculate the result of number A plus number B. Calculate the result of number A minus number B. Calculate the result of number A multiplied by number B. / Number A/Number B Calculate the result of number A divided by number B. FAQs Troubleshooting Process Context This topic describes how to locate faults that occur when the Interactive Self Analysis Explorer (ISAE) extracts data and performs exploratory analysis. Figure 1-24 shows the data flow of the ISAE.

41 Figure 1-24 Data flow of the ISAE The data flow is described as follows: The Data Obtaining module of the ISAE obtains data models from the DG. The Data Obtaining module designs data extraction rules, accesses the data sources through the UDA, and generates data sets. The Data Obtaining module invokes the capability provided by the UDA to load the data sets from the external data source to the MOLAP analysis engine (SparkSQL) for the Exploratory Analysis module to use. The Exploratory Analysis module designs analysis based on the data sets, MOLAP analysis engine, and SmartMiner, and automatically performs data exploration and analysis. The Exploratory Analysis module queries data from the UDA when required. Analysis results can be saved as applications and released to the application market.

42 Possible Cause Table 1-6 Common faults Scna Possible Cause Impact Failure to extract data sets Failure to explore and analyze data in real time Data models are abnormal. The data source is abnormal. (The data source of data models is abnormal or the data source contains no data.) The UDA is abnormal. Data sets are abnormal. The UDA is abnormal. The SparkSQL is abnormal. The ISAE cannot obtain data models from the DG and therefore cannot extract data. The ISAE cannot connect to the data source to obtain required data. The ISAE cannot connect to the UDA and cannot obtain required data from the data source. The ISAE cannot perform data set-based exploration and analysis. The ISAE cannot connect to the UDA and cannot obtain required data from the data source. The SparkSQL of the FusionInsight Hadoop is abnormal and the ISAE cannot perform real-time data exploration. Troubleshooting Process Figure 1-25 shows the troubleshooting process.

43 Figure 1-25 Troubleshooting process If the ISAE is not running properly, you can collect the following logs and perform troubleshooting based on Error information in the logs. Table 1-7 Log types and storage paths Log Type Log File Description Storage Directory Debug log as_debug.log Debug logs generated when the ISAE is running. ${HOME}/logs/debug of the ISAE installation user bme.log Debug logs of the BME framework. ${HOME}/logs/debug of the ISAE installation user