Define workbench

define workbench

Workbench definition. Contents. Example definition; Workbench properties. Workbench is a view that displays content items in a workspace. Existing connection lines are highlighted when you use a pointer device to hover on different database objects. The following table defines the meaning of each. Find 8 ways to say WORKBENCH, along with antonyms, related words, and example sentences at, See definition of workbench on MANAGEENGINE DESKTOP CENTRAL 6 0 CRACK Связала из плотных вязании на 20. из плотных пакетов. 15-19.

It is part of the content app framework, typically defined in the browser subapp. The workbench contains a list of content views. Common view types are tree, list and thumbnail. This workbench definition is part of the Magnolia 6 UI framework.

The fully qualified class name is info. Parent node for the content view definitions. Defines how users can view content in the workbench. Must contain at least one content view. For more information, see Content view definition. You can see an example extension view configured to display analytics data in the Pages app in the Analytics Connector Pack documentation. Actual view definition to be displayed in the extension panel. Must implement the info.

ViewDefinition interface. The data object's role property if present will be translated into the " org. The data object's type safe property if present will be translated into the " org. TypeSafe Java annotation. The data object's class reactive property if present will be translated into the " org. ClassReactive Java annotation. The data object's property reactive property if present will be translated into the " org. PropertyReactive Java annotation. The data object's timestamp property if present will be translated into the " org.

Timestamp Java annotation. The data object's duration property if present will be translated into the " org. Duration Java annotation. The data object's expires property if present will be translated into the " org. Expires Java annotation. The data object's remotable property if present will be translated into the " org.

Remotable Java annotation. A standard Java default or no parameter constructor is generated, as well as a full parameter constructor, i. The data object's user-defined fields are translated into Java class fields, each one of them with its own getter and setter method, according to the following transformation rules:. The data object field's identifier will become the Java field identifier. The data object field's type is directly translated into the Java class's field type. In case the field was declared to be multiple i.

List" type. The equals property: when it is set for a specific field, then this class property will be annotated with the " org. Key" annotation, which is interpreted by the Drools Engine, and it will 'participate' in the generated equals method, which overwrites the equals method of the Object class. The latter implies that if the field is a 'primitive' type, the equals method will simply compares its value with the value of the corresponding field in another instance of the class.

If the field is a sub-entity or a collection type, then the equals method will make a method-call to the equals method of the corresponding data object's Java class, or of the java. List standard Java class, respectively. If the equals property is checked for ANY of the data object's user defined fields, then this also implies that in addition to the default generated constructors another constructor is generated, accepting as parameters all of the fields that were marked with Equals.

Furthermore, generation of the equals method also implies that also the Object class's hashCode method is overwritten, in such a manner that it will call the hashCode methods of the corresponding Java class types be it 'primitive' or user-defined types for all the fields that were marked with Equals in the Data Model. The position property: this field property is automatically set for all user-defined fields, starting from 0, and incrementing by 1 for each subsequent new field.

However the user can freely changes the position among the fields. At code generation time this property is translated into the " org. Position" annotation, which can be interpreted by the Drools Engine. Also, the established property order determines the order of the constructor parameters in the generated Java class. Note that the two of the data object's fields, namely 'header' and 'lines' were marked with Equals, and have been assigned with the positions 2 and 1, respectively. Using an external model means the ability to use a set for already defined POJOs in current project context.

Once the dependency has been added the external POJOs can be referenced from current project data model. Dependency to a JAR file already installed in current local M2 repository typically associated the the user home. If the uploaded file is not a valid Maven JAR don't have a pom. Open the project editor see bellow and click on the "Add from repository" button to open the JAR selector to see all the installed JAR files in current "Guvnor M2 repository".

When the desired file is selected the project should be saved in order to make the new dependency available. When a dependency to an external JAR has been set, the external POJOs can be used in the context of current project data model in the following ways:.

The following screenshot shows how external objects are prefixed with the string " -ext- " in order to be quickly identified. Current version implements roundtrip and code preservation between Data modeller and Java source code. No matter where the Java code was generated e. Also whatever Type or Field annotation not managed by the Data Modeler will be preserved when the Java sources are updated by the Data modeller.

Aside from code preservation, like in the other workbench editors, concurrent modification scenarios are still possible. Common scenarios are when two different users are updating the model for the same project, e. From an application context's perspective, we can basically identify two different main scenarios:.

In this scenario the application user has basically just been navigating through the data model, without making any changes to it. Meanwhile, another user modifies the data model externally. In this case, no immediate warning is issued to the application user. However, as soon as the user tries to make any kind of change, such as add or remove data objects or properties, or change any of the existing ones, the following pop-up will be shown:. Re-open the data model, thus loading any external changes, and then perform the modification he was about to undertake, or.

Ignore any external changes, and go ahead with the modification to the model. In this case, when trying to persist these changes, another pop-up warning will be shown:. The "Force Save" option will effectively overwrite any external changes, while "Re-open" will discard any local changes and reload the model.

The application user has made changes to the data model. Meanwhile, another user simultaneously modifies the data model from outside the application context. In this alternative scenario, immediately after the external user commits his changes to the asset repository or e.

Re-open the data model, thus losing any modifications that where made through the application, or. The user tries to persist the changes he made to the model by clicking the "Save" button in the data modeller top level menu. This leads to the following warning message:. A data set is basically a set of columns populated with some rows, a matrix of data composed of timestamps, texts and numbers. A data set can be stored in different systems: a database, an excel file, in memory or in a lot of other different systems.

On the other hand, a data set definition tells the workbench modules how such data can be accessed, read and parsed. Notice, it's very important to make crystal clear the difference between a data set and its definition since the workbench does not take care of storing any data, it just provides an standard way to define access to those data sets regardless where the data is stored.

Let's take for instance the data stored in a remote database. A valid data set could be, for example, an entire database table or the result of an SQL query. In both cases, the database will return a bunch of columns and rows. Now, imagine we want to get access to such data to feed some charts in a new workbench perspective. First thing is to create and register a data set definition in order to indicate the following:. This chapter introduces the available workbench tools for registering and handling data set definitions and how this definitions can be consumed in other workbench modules like, for instance, the Perspective Editor.

For simplicity sake we will be using the term data set to refer to the actual data set definitions as Data set and Data set definition can be considered synonyms under the data set authoring context. The center panel, shows a welcome screen, whilst the left panel contains the Data Set Explorer listing all the data sets available.

This perspective is only intended to Administrator users, since defining data sets can be considered a low level task. The Data Set Explorer list the data sets present in the system. Every time the user clicks on the data set it shows a brief summary alongside the following information:.

Once clicked the Data set editor screen is opened on the center panel. Clicking on the New Data Set button opens a new screen from which the user is able to create a new data set definition in three steps:. Specify the attributes for being able to look up data from the remote system. The configuration varies depending on the data provider type selected. This screen lists all the current available data provider types and helper popovers with descriptions.

Each data provider is represented with a descriptive image:. The provider type selected in the previous step will determine which configuration settings the system asks for. The UUID attribute is a read only field as it's generated by the system.

It's only intended for usage in API calls or specific operations. After clicking on the Test button see previous step , the system executes a data set lookup test call in order to check if the remote system is up and the data is available.

If everything goes ok the user will see the following screen:. This screen shows a live data preview along with the columns the user wants to be part of the resulting data set. The user can also navigate through the data and apply some changes to the data set structure. Once finished, we can click on the Save button in order to register the new data set definition. We can also change the configuration settings at any time just by going back to the configuration tab. In the Columns tab area the user can select what columns are part of the resulting data set definition.

Select only those columns you want to be part of the resulting data set. Label - For text values supporting group operations similar to the SQL "group by" operator which means you can perform data lookup calls and get one row per distinct value. Text - For text values NOT supporting group operations.

Typically for modeling large text columns such as abstracts, descriptions and the like. Number - For numeric values. It does support aggregation functions on data lookup calls: sum, min, max, average, count, disctinct. Date - For date or timestamp values. It does support time based group operations by different time intervals: minute, hour, day, month, year, No matter which remote system you want to retrieve data from, the resulting data set will always return a set of columns of one of the four types above.

There exists, by default, a mapping between the remote system column types and the data set types. The user is able to modify the type for some columns, depending on the data provider and the column type of the remote system. The system supports the following changes to column types:. For instance, imagine a database table called "document" containing a large text column called "abstract".

As we do not want the system to treat such column as a "label" we might change its column type to "text". Doing so, we are optimizing the way the system handles the data set and. This can be used for instance to indicate that a given numeric column is not a numeric value that can be used in aggregation functions.

Despite its values are stored as numbers we want to handle the column as a "label". One example of such columns are: an item's code, an appraisal id. BEAN data sets do not support changing column types as it's up to the developer to decide which are the concrete types for each column. A data set definition may define a filter. The goal of the filter is to leave out rows the user does not consider necessary.

The filter feature works on any data provider type and it lets the user to apply filter operations on any of the data set columns available. While adding or removing filter conditions and operations, the preview table on central area is updated with live data that reflects the current filter status. There exists two strategies for filtering data sets and it's also important to note that choosing between the two have important implications. Imagine a dashboard with some charts feeding from a expense reports data set where such data set is built on top of an SQL table.

Imagine also we only want to retrieve the expense reports from the "London" office. This is the recommended approach. Another option is to define a data set with no initial filter and then let the individual charts to specify their own filter. It's up to the user to decide on the best approach. Depending on the case it might be better to define the filter at a data set level for reusing across other modules.

The decision may also have impact on the performance since a filtered cached data set will have far better performance than a lot of individual non-cached data set lookup requests. See the next section for more information about caching data sets.

Notice, for SQL data sets, the user can use both the filter feature introduced or, alternatively, just add custom filter criteria to the SQL sentence. Although, the first approach is more appropriated for non technical users since they might not have the required SQL language skills. To edit an existing data set definition go the data set explorer, expand the desired data set definition and click on the Edit button.

This will cause a new editor panel to be opened and placed on the center of the screen, as shown in the next screenshot:. Delete - To remove permanently from storage the data set definition. Any client module referencing the data set may be affected.

Validate - To check that all the required parameters exists and are correct, as well as to validate the data set can be retrieved with no issues. Any action performed is registered in the repository logs so it is possible to audit the change log later on.

In the Advanced settings tab area the user can specify caching and refresh settings. Those are very important for making the most of the system capabilities thus improving the performance and having better application responsive levels. The system provides caching mechanisms out-of-the-box for holding data sets and performing data operations using in-memory strategies.

The use of these features brings a lot of advantages, like reducing the network traffic, remote system payload, processing times etc. On the other hand, it's up to the user to fine tune properly the caching settings to avoid hitting performance issues. Any data look up call produces a resulting data set, so the use of the caching techniques determines where the data lookup calls are executed and where the resulting data set is located.

If ON then the data set involved in a look up operation is pushed into the web browser so that all the components that feed from this data set do not need to perform any requests to the backend since data set operations are resolved at a client side:. Data set operations grouping, aggregations, filters and sort are processed within the web browser, by means of a Javascript data set operation engine.

If you know beforehand that your data set will remain small, you can enable the client cache. It will reduce the number of backend requests, including the requests to the storage system. On the other hand, if you consider that your data set will be quite big, disable the client cache so as to not hitting with browser issues such as slow performance or intermittent hangs.

This feature allows to reduce the number of requests to the remote storage system , by holding the data set in memory and performing group, filter and sort operations using the in-memory engine. It's useful for data sets that do not change very often and their size can be considered acceptable to be held and processed in memory. It can be also helpful on low latency connectivity issues with the remote storage.

On the other hand, if your data set is going to be updated frequently, it's better to disable the backend cache and perform the requests to the remote storage on each look up request, so the storage system is in charge of resolving the data set lookup request.

BEAN and CSV data providers relies by default on the backend cache, as in both cases the data set must be always loaded into memory in order to resolve any data lookup operation using the in-memory engine. This is the reason why the backend settings are not visible in the Advanced settings tab. The refresh feature allows for the invalidation of any cached data when certain conditions are meet. The data set refresh policy is tightly related to data set caching, detailed in previous section.

This invalidation mechanism determines the cache life-cycle. Source data changes predictable - Imagine a database being updated every night. That way, the system will always invalidate the cached data set every day. This is the right configuration when we know in advance that the data is going to change. If so the system, before invalidating any data, will check for modifications. On data modifications, the system will invalidate the current stale data set so that the cache is populated with fresh data on the next data set lookup call.

Real time scenarios - In real time scenarios caching makes no sense as data is going to be updated constantly. In this kind of scenarios the data sent to the client has to be constantly updated, so rather than enabling the refresh settings remember this settings affect the caching, and caching is not enabled it's up to the clients consuming the data set to decide when to refresh.

As we already know, Workbench provides a set of editors to author assets in different formats. One additional feature provided by Workbench is the ability to embed it in your own Web Applications thru it's standalone mode. So, if you want to edit rules, processes, decision tables, etc All the features described here are entirely optional, but the usage is recommended if you are planning to have multiple projects.

All the Asset Management features try to impose good practices on the repository structure that will make the maintainace, versioning and distribution of the projects simple and based on standards. All the Asset Management features are implemented using jBPM Business Processes, which means that the logic can be reused for external applications as well as adapted for domain specific requirements when needed. Since the creation of the assets management features repositories can be classified into Managed or Unmanaged.

All new assets management features are available for this type of repositories. Additionally a managed repository can be "Single Project" or "Multi Project". A "Single Project" managed repository will contain just one Project. And a "Multi Project" managed repository can contain multiple Projects. All of them related through the same parent, and they will share the same group and version information.

Assets management features are not available for this type or repositories and they basically behaves the same as the repositories created with previous workbench versions. The Configure Repository process is in charge of the post initialization of the repository. This process will be automatically triggered if the user selects to create a Managed Repository on the New repository wizzard. If they decide to use the governance feature the process will kick in and as soon as the repository is created.

A new development and release branches will be created. Notice that the first time that this process is called, the master branch is picked and both branches dev and release will be based on it. By default the asset management feature is not enabled so make sure to select Managed Repository on the New Repository Wizzard.

When we work inside a managed repository, the development branch is selected for the users to work on. If multiple dev branches are created, the user will need to pick one. When some work is done in the developments branch and the users reach a point where the changes needs to be tested before going into production, they will start a new Promote Changes process so a more technical user can decide and review what needs to be promoted.

The users belonging to the "kiemgmt" group will see a new Task in their Group Task List which will contain all the files that had being changed. The user needs to select the assets that will be promoting via the UI.

The underlying process will be cherry-picking the commits selected by the user to the release branch. The user can specify that a review is needed by a more technical user. This process can be repeated multiple times if needed before creating the artifacts for the release. The Build process can be triggered to build our projects from different branches. This allows us to have a more flexible way to build and deploy our projects to different runtimes. This process will build the project calling the Build Process and it will update all the maven artifacts to the next version.

This section describes the common usage flow for the asset management features showing all the screens involved. When a managed repository is created the assets management configuration process is automatically launched in order to create the repository branches, and the corresponding project structure is also created. The branch selector lets to switch between the different branches created by the Configure Repository Process.

From the repository structure screen it's also possible to create, edit or delete projects from current repository. Filling the parameters bellow a new instance of the Configure Repository can be started. Filling the parameters bellow a new instance of the Promote Changes Process can be started. Filling the parameters bellow a new instance of the Release Process can be started. Chapter 9. Installation 9. War installation 9. Workbench data 9. System properties 9.

Quick Start 9. Add repository 9. Add project 9. Define Data Model 9. Define Rule 9. Build and Deploy 9. Administration 9. Administration overview 9. Organizational unit 9. Repositories 9. Configuration 9. User management 9. Roles 9. Restricting access to repositories 9.

Command line config tool 9. Introduction 9. Log in and log out 9. Home screen 9. Workbench concepts 9. Initial layout 9. Changing the layout 9. Resizing 9. Repositioning 9. Authoring 9. Artifact Repository 9. Asset Editor 9. Tags Editor 9. Project Explorer 9. Project Editor 9. Validation 9. Data Modeller 9. Data Sets 9. Embedding Workbench In Your Application 9. Asset Management 9. Asset Management Overview 9. Managed vs Unmanaged Repositories 9.

Asset Management Processes 9. Usage Flow 9. Repository Structure 9. Managed Repositories Operations 9. Remote APIs. War installation. Note Oracle WebLogic requires additional configuration to correctly install the Workbench. Workbench data. Note In production, make sure to back up the workbench data directory. System properties. Default: working directory org. Default: true org. Default: localhost org. This is of the form host1:port1,host2:port2,host3:port3 , for example: localhost org.

Default: false org. Default: ApplicationRealm org. Quick Start. Add repository. Selecting Administration perspective. Creating new repository. Add project. Selecting Authoring perspective. Creating new project. Entering project name. Group ID follows Maven conventions.

Artifact ID is pre-populated from the project name. Version is set as 1. Entering project details. Define Data Model. Note You can also use types contained in existing JARs. Please consult the full documentation for details. Creating "Data Object". Creating a new type. Click "Create" and add the field. Clicking "Save". Define Rule. Entering file name for rule. Defining a rule. Saving the rule. Build and Deploy. Selecting "Project Editor".

Building and deploying a project. Administration overview. Organizational unit. Warning Never clone your repositories directly from. Repository Editor. User management. Manages rules, models, process flows, forms and dashboards Manages the asset repository Can create, build and deploy projects Can use the JBDS connection to view processes.

Business user. Does process management Handles tasks and dashboards. Only has access to dashboards. Restricting access to repositories. Command line config tool. Config Tool Modes. All changes are made locally and published to upstream when: "push-changes" command is explicitly executed "exit" is used to close the tool.

Available Commands. Table 9. How to use. Log in and log out. Home screen. Workbench concepts. Part A Part is a screen or editor with which the user can interact to perform operations. Panel A Panel is a container for one or more Parts. Panels can be resized. Perspective A perspective is a logical grouping of related Panels and Parts.

Initial layout. The Workbench. Project Explorer This provides the ability for the user to browse their configuration; of Organizational Units in the above "example" is the Organizational Unit , Repositories in the above "uf-playground" is the Repository and Project in the above "mortgages" is the Project.

Problems This provides the user will real-time feedback about errors in the active Project. Empty space This empty space will contain an editor for assets selected from the Project Explorer. Other screens will also occupy this space by default; such as the Project Editor. Changing the layout. Repositioning - dragging. Repositioning - complete. Artifact Repository. Note This remote Maven repository is relatively simple.

Asset Editor. The views A : The editing area - exactly what form the editor takes depends on the Asset type. C : Different views for asset content or asset information. Editor shows the main editor for the asset Overview contains the metadata and conversation views for this editor. Config contains the model imports used by the asset.

The Asset Editor - Editor tab. A : General information about the asset and the asset's description. C : Meta data from the "Dublin Core" standard D : Comments regarding the development of the Asset can be recorded here. The Asset Editor - Overview tab. A : Meta data:- "Tags:" A tagging system for grouping the assets. The Metadata tab. Tags Editor.

Creating Tags. Existing Tags. Project Explorer. Initial view. An empty initial view. Selecting a repository. Showing packages. Expanded asset group. Different views. Project View A simplified view of the underlying project structure. Repository View A complete view of the underlying project structure including all files; either user-defined or system generated. Switching view. Project View examples.

Project View - Folders. Project View - Links. Repository View examples. Repository View - Folders. Repository View - Links. Download Project or Repository. Repository and Project Downloads. Branch selector. Filtering by Tag. Enabling Filter by Tag. Filter by Tag. Copy, Rename, Delete and Download Actions. Project View - Package actions. Repository View - Files and directories actions. Warning Workbench roadmap includes a refactoring and an impact analyses tools, but currenctly doesn't have it.

Important Files locked by other users as well as directories that contain such files cannot be renamed or deleted until the corresponding locks are released. Project Editor. Project Screen and the different views. Project Settings. Project General Settings. Knowledge Base Settings. Note For more information about the Knowledge Base properties, check the Drools Expert documentation for kmodule. Knowledge bases and sessions. Knowledge base list. Knowledge base properties. Knowledge sessions. Import Suggestions.

Note Unlike in the previous version of Guvnor. Problem Panel. The Problems Panel shows the validation errors. On demand validation. Data Modeller. First steps to create a data model. Go to authoring perspective and select a project. Click on a Data Object. Data modeller overview. New field creation. The Data Object's field browser. Data Object selector. Data Object general properties.

Field selector. Field general properties. Data modeller Tool Bar. Persistence tool window. Advanced tool window. Source editor. Data Objects. New Data Object menu option. A field type mandatory : each data object field needs to be assigned with a type.

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Send us feedback. See more words from the same year. Accessed 2 Apr. More Definitions for workbench. Nglish: Translation of workbench for Spanish Speakers. Subscribe to America's largest dictionary and get thousands more definitions and advanced search—ad free! Log in Sign Up. Save Word. Definition of workbench. Examples of workbench in a Sentence Recent Examples on the Web At another workbench a couple yards away, Bennett is installing the back braces for a guitar.

First Known Use of workbench circa , in the meaning defined above. Learn More About workbench. Time Traveler for workbench The first known use of workbench was circa See more words from the same year. Statistics for workbench Look-up Popularity. Style: MLA. Kids Definition of workbench. Get Word of the Day daily email! Test Your Vocabulary. This is typically true of specialized topics which are used only in specific texts but not otherwise.

The second table provides word-level means and standard deviations. These values are extremely useful in identifying the most frequently used words within each category. For instance, in the example below, "learn" was the most-used word in the "school" category. You could modify the dictionary as needed and re-evaluate the internal consistency and word means.

PROTIP: Given the statistical properties of language, it is well-understood that often, a few words are going to be the largest contributors to any particular dictionary category. When it comes to benchmarking your dictionary and assessing its psychometric properties, it is reasonable to ask "What dataset should I use?

You can certainly assess the internal consistency of your dictionary on the dataset that you intend to apply the dictionary to later — this is perhaps the most straight-forward way to determine whether your dictionary has adequate internal consistency on the data where it counts the most. Indeed, this is similar to calculating Cronbach's alpha on questionnaire data: you are effectively taking the already-collected data and evaluating your measure's properties. However, it is also the case that if you are tailoring your dictionary to perform well on your existing dataset, you can run the risk of "overfitting" your dictionary to a particular context or sample.

Often, we instead might want to look to another dataset or multiple datasets to benchmark our dictionary and ensure that it is robust and not overfit to a single context. If we're looking to evaluate our dictionary on an out-of-sample dataset, which one should we use? There is no objectively correct answer here but, as a general rule, you will want to find some data that has similar properties to the context s in which you imagine your dictionary will be used.

For example, are your texts formal or informal? Are they SMS, e-mails, poems, etc.? Or, if you want to evaluate your dictionary irrespective of context, you might need to evaluate its psychometric properties across several datasets. Today, language data is more accessible than ever before. There are a number of available datasets that you can use as a starting point for benchmarking your data e. Often, you will find yourself wanting some help in coming up with words to include in your dictionary — trust us, we know how much work it can be to create a dictionary from nothing.

In the Add Dictionary Words tab, you can type in a list of words that you believe best represent the concept or category that you are trying to capture. LIWC will automatically generate words that are most similar in meaning to the list of words you typed in. You can then easily go through the list of words generated by LIWC and select the words you would like to add to your dictionary.

We currently have plans to add additional domain-specific models e. Understanding and Measuring the Dictionary Workbench LIWC comes with an entirely new Dictionary Workbench feature that helps you to create and evaluate your own dictionary files. Evaluate Internal Consistency The Evaluate Internal Consistency feature of the Dictionary Workbench can be used to evaluate some of the standard psychometric properties of your dictionary.

Benchmark Datasets When it comes to benchmarking your dictionary and assessing its psychometric properties, it is reasonable to ask "What dataset should I use?

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