vania-janet/multiturn-rag-retrieval-data huggingface dataset:
[Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) 
 Mass Data Migration ![Checkmark icon](https://cloud.ibm.com/docs-content/v1/content/6f3c07c34058f637b1c86044ca4f9b24214330a7/activity-tracker/images/checkmark-icon.svg) 



[] IBM Cloud Object Storage (COS) generates global, management, and data events.



* By default, events that report on global actions such as the creation of a bucket are collected automatically. You can monitor these events through the Activity Tracker instance that is located in the Frankfurt location.
* Collection of management and data events in your account is optional.
* You must configure each bucket to enable management events, or management and data events. You cannot enable data events only for a bucket.



* To monitor management events, you must configure a bucket and specify the Activity Tracker instance where those events will be available for monitoring.
* To monitor data events, you must select the option Track data events when you associate a bucket with an Activity Tracker instance.









 Web and mobile services 

America

Asia Pacific

Europe



Table 13. Web and mobile services integration in America locations

 Service Dallas (us-south) Washington (us-east) Toronto (ca-tor) Sao Paulo (br-sao) 

 Mobile Foundation !
In this tutorial, you will build a log analysis pipeline designed to collect, store and analyze log records to support regulatory requirements or aid information discovery. This solution leverages several services available in IBM Cloud: Event Streams, Object Storage, Data Engine (previously SQL Query), Key Protect, and IBM Analytics Engine. A script and tool will assist you to simulate the transmission of web server log messages from a static file to Event Streams.

Object Storage Event Streams

+3

Analytics Engine,Data Engine,Key Protect



* 3 hours
* 2023-05-05



Internet of Things[Getting started with IBM Maximo Application Suite Dedicated](https://cloud.ibm.com/docs/mas-ms?topic=mas-ms-getting-started-with-ibm-maximo-application-suite-dedicated)Getting started with IBM Maximo Application Suite Dedicated

{{ shortdesc }}

Maximo Application Suite Dedicated



* 10 minutes
* 2023-02-02



[Getting started with IBM Maximo Application Suite as a Service](https://cloud.ibm.com/docs/mas-saas?topic=mas-saas-getting-started-with-ibm-maximo-application-suite-saas)Getting started with IBM Maximo Application Suite as a Service

Maximo Application Suite as a Service (MAS SaaS) provides IBM managed provisioning, installation, configuration, operational support, security, maintenance and administration of Maximo Application Suite for clients on the Amazon Web Services (AWS) platform. MAS SaaS combines key technologies, IBM Maximo Application Suite, Red Hat Open Shift on AWS (ROSA) and IBM DB2 Warehouse, into a focused MAS services solution, allowing clients to make implementation of product functionality & features a priority.

Maximo Application Suite as a Service (MAS-SaaS)



* 10 minutes
* 2023-06-06



Logging and monitoring[Getting started with Activity Tracker hosted event search offerings](https://cloud.ibm.com/docs/activity-tracker?topic=activity-tracker-getting-started-search)Getting started with Activity Tracker hosted event search offerings

Use the IBM Cloud® Activity Tracker service to monitor the activity of your IBM Cloud account.
Configuring connector and seed options 

The Data Crawler is no longer supported or available for download beginning [17 April 2019](https://cloud.ibm.com/docs/discovery?topic=discovery-release-notes17apr19). This content is provided for existing installations only. See [Connecting to Data Sources](https://cloud.ibm.com/docs/discovery?topic=discovery-sources) for other available connectivity options.

When crawling data, the Crawler first identifies the type of data repository (connector) and the user-specified starting location (seed) to begin downloading information.

When using the Data Crawler, data repository security settings are ignored.

Seeds are the starting points of a crawl, and are used by the Data Crawler to retrieve data from the resource that is identified by the connector. Typically, seeds configure URLs to access protocol-based resources such as fileshares, SMB shares, databases, and other data repositories that are accessible by various protocols. Moreover, different seed URLs have different capabilities. Seeds can also be repository-specific, to enable crawling of specific third-party applications such as customer relationship management (CRM) systems, product life cycle (PLC) systems, content management systems (CMS), cloud-based applications, and web database applications.

To crawl your data correctly, you must ensure that the Crawler is properly configured to read your data repository. The Data Crawler provides connectors to support data collection from the following repositories:



* [Filesystem](https://cloud.ibm.com/docs/discovery?topic=discovery-configuring-connector-and-seed-optionsfilesystem-crawl-options)
* [Databases, via JDBC](https://cloud.ibm.com/docs/discovery?topic=discovery-configuring-connector-and-seed-optionsdatabase-crawl)
* [CMIS (Content Management Interoperability Services)](https://cloud.ibm.com/docs/discovery?topic=discovery-configuring-connector-and-seed-optionscmis-crawl-options)
[Mass Data Migration](https://cloud.ibm.com/docs/mass-data-migration?topic=mass-data-migration-getting-started-tutorial) mass-data-migration [Events](https://cloud.ibm.com/docs/mass-data-migration?topic=mass-data-migration-at-events) 



[] IBM Cloud Object Storage (COS) generates global, and location-based events.



* By default, bucket management events such as the creation of a bucket are collected automatically.
* Collection of data events in your account is optional. You must configure each bucket to enable data events by selecting the option Track data events.
* Mass Data Migration generates global events only, for each transition of the order state.







 VMware Solutions 

With IBM Cloud for VMware Solutions, you can quickly and seamlessly integrate or migrate your on-premises VMware workloads to the IBM Cloud by using the scalable, secure, and high-performance IBM Cloud infrastructure and the industry-leading VMware hybrid virtualization technology. You can easily deploy your VMware virtual environments and manage the infrastructure resources on IBM Cloud. At the same time, you can still use your familiar native VMware product console to manage the VMware workloads. [Learn more](https://www.ibm.com/cloud/vmware).

The following table lists VMware solution services that send auditing events:



Table 12. List of VMware solution services

 Service CRN service name Events 

 [IBM Cloud for VMware Solutions](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-vc_vcenterserveroverview) vmware-solutions [Global events](https://cloud.ibm.com/docs/vmwaresolutions?topic=vmwaresolutions-at-eventsat-events)
Role Description 

 Administrator As an administrator, you can perform all platform actions based on the resource this role is being assigned, including assigning access policies to other users. 







 DataStage 

Review the available platform and service roles and the actions mapped to each to help you assign access. If you're using the CLI or API to assign access, use datastage for the service name.

Platform roles

Service roles

Actions



Table 47. Platform roles - DataStage
Use the tab buttons to change the context of the table. This table has row and column headers. The row headers provide the platform role name and the column headers identify the specific information available about each role.

 Role Description 

 Administrator As an administrator, you can perform all platform actions based on the resource this role is being assigned, including assigning access policies to other users. 
 Editor As an editor, you can perform all platform actions except for managing the account and assigning access policies. 
 Operator As an operator, you can perform platform actions required to configure and operate service instances, such as viewing a service's dashboard. 







 Direct Link 

Review the available platform and service roles and the actions mapped to each to help you assign access. If you're using the CLI or API to assign access, use directlink for the service name.

No supported roles.





 Direct Link Connect 

Review the available platform and service roles and the actions mapped to each to help you assign access. If you're using the CLI or API to assign access, use directlink.connect for the service name.

Platform roles

Actions



Table 49. Platform roles - Direct Link Connect
Use the tab buttons to change the context of the table. This table has row and column headers. The row headers provide the platform role name and the column headers identify the specific information available about each role.

 Role Description 

 Administrator As an administrator, you can perform all platform actions based on the resource this role is being assigned, including assigning access policies to other users. 
 Editor As an editor, you can perform all platform actions except for managing the account and assigning access policies.
Analyzing time series data 

A time series is a sequence of data values that are measured at successive, though not necessarily regular, points in time. You can use special SQL functions to process time series data, for example, to identify trends and to predict future values based on these trends.

See the following examples of time series:



* Stock share prices and trading volumes.
* Electrocardiogram (ECG) data.
* Temperature or seismographic data.
* Network performance measurements.
* Electricity usage as recorded by a smart meter and reported through an Internet of Things data feed.
* A clickstream log that records, for an individual computer user, which buttons were clicked, which pages or websites were visited, and other information of interest.



Each entry in a time series is called an observation. Each observation comprises a timetick (a 64-bit integer that indicates when the observation was made) and the data that is recorded for that observation. The recorded data can be either numerical (for example, temperature or stock share price) or categorical (for example, geographic area). A time series can be, but must not necessarily be, associated with a [time reference system (TRS)](https://cloud.ibm.com/docs/services/sql-query?topic=sql-query-TRS), which defines the granularity of each timetick and the start time.

You can use a set of integrated SQL [functions](https://cloud.ibm.com/docs/services/sql-query?topic=sql-query-artifact) and [data types](https://cloud.ibm.com/docs/services/sql-query?topic=sql-query-ts_datatypes) to process time series data. The functions are of the following types:

Artifact creation
Create time series, TRSs, and other artifacts that are used during the processing of time series data.

Exploding and flattening
Convert time series data to tabular data.

Summary
Retrieve information about a time series.

Statistical
Derive statistical insight from time series data. Each statistical function is of one of the following types:



* A transform produces, as output, a new time series.
Data processing functions 

Each of these functions evaluates or manipulates input data and produces output data.



 Exploding and flattening functions 

Each of these functions converts time series data to tabular data.

TS_EXPLODE(DoubleTimeSeries)
: Output: Rows of Long, Double
: Converts each observation of the input data into a two-column row that comprises a timetick of type Long and a value of type Double.

TS_S_EXPLODE(StringTimeSeries)
: Output: Rows of Long, String
: Converts each observation of the input data into a two-column row that comprises a timetick of type Long and a value of type String.

TS_DA_EXPLODE(DoubleArrayTimeSeries)
: Output: Rows of Long, DoubleArray
: Converts each observation of the input data into a two-column row that comprises a timetick of type Long and a value of type DoubleArray.

TS_SA_EXPLODE(StringArrayTimeSeries)
: Output: Rows of Long, StringArray
: Converts each observation of the input data into a two-column row that comprises a timetick of type Long and a value of type StringArray.

TS_FLATTEN(SegmentTimeSeries)
: Output: Rows of Long, Integer, TimeSeries
: Converts a segment time series into a series of rows, with there being one row for each segment:



* A Long value that specifies the timetick of the first observation in the segment.
* An integer that indicates the index number (0 to n) of the segment.
* A time series that contains the observations that comprise the segment.



: The type of the output time series corresponds to the type of the input segment time series.





 Summary functions 

Each of these functions retrieves information about a time series.

TS_DESCRIBE(DoubleTimeSeries)
: Output: Statistics for time series
: Returns a set of columns with timing statistics, value statistics, and numeric statistics.

TS_COUNT(TimeSeries)
: Output: Integer
: Returns the number of observations in a time series.

TS_COUNT_ANCHOR(TimeSeries, AnchorType)
: Output: Integer
Data residency 

Because blockchain networks are oblivious to the type of data that is processed, extra steps must sometimes be taken to keep certain kinds of data secure. The most common requirement on data residency is associated with laws within certain countries, which mandate that all data that is processed and stored in an IT system must remain within a specific country’s borders. Similarly, some companies in highly regulated industries, such as government, healthcare, and financial services, require that data must be stored entirely behind their firewall.

Blockchain networks allow multiple organizations to use a distributed ledger to transact and share data in a way that is trusted and secure. However, this implies that data can be distributed across the nodes of the network and the regions where those nodes reside. Organizations can use several options to separate data from the rest of the network and achieve data residency:



1. [Private data collections on a shared channel](https://cloud.ibm.com/docs/blockchain?topic=blockchain-console-icp-about-data-residencyconsole-icp-about-data-residency-fabric)
2. [Private data collections on a separate channel](https://cloud.ibm.com/docs/blockchain?topic=blockchain-console-icp-about-data-residencyconsole-icp-about-data-residency-use-case)
3. [A separate channel with all of the nodes on the channel within a single country](https://cloud.ibm.com/docs/blockchain?topic=blockchain-console-icp-about-data-residencyconsole-icp-about-data-residency-use-case-channel)
4. [A separate channel with only ordering nodes from one country](https://cloud.ibm.com/docs/blockchain?topic=blockchain-console-icp-about-data-residencyconsole-icp-about-data-residency-raft)



Each approach provides an increased level of isolation and protection for your data, but requires additional effort to implement and manage.
Learning about IBM Log Analysis architecture and workload isolation 

Review the following sample architecture for IBM Log Analysis, and learn more about different isolation levels so that you can choose the solution that best meets the requirements of the workloads that you want to run in the cloud.



 IBM Log Analysis architecture 

IBM Log Analysis is a multi-tenant, regional service that is available in IBM Cloud. You can use it to monitor your applications, platform resources, and infrastructure.

Zoom

![IBM Log Analysis](https://cloud.ibm.com/docs-content/v1/content/f61dbc5d84bf0c89e477a05cab04c72f429e1b7a/log-analysis/images/Log-Analysis-01-Architecture.svg)

Figure 1. Example services integration scenario.

The API server component provides a web and an API interface to the logging service.

The collector component ingest data that logging agents forward to the logging service.

The datastore component stores data, alerts, and user metadata.

You can use logging agents to monitor and collect logs from hosts such as a Kubernetes cluster or a Linux system. A logging agent connects to 1 logging instance. The agent forwards data to the instance that is connected.

Platform logs are collected automatically for enabled-logging services in each region. The data is forwarded to the IBM Log Analysis service instance that is enabled to collect and monitor platform metrics in a region.

The logging web UI is the front-end component where users can monitor and manage logs from hosts through dashboards, views, screens, and alerts.





 IBM Log Analysis workload isolation 

Each regional deployment of the IBM Log Analysis service serves multiple tenants that are identified by the IBM service instance.



* There is 1 IBM Log Analysis service per region that is responsible for running user workloads in the region.
* The logging data that is collected and processed by the IBM Log Analysis service is associated with that location and not visible to the other regions by virtue of this association.
* Within a service instance, logging data is isolated per logging instance within a region.
* The IBM Log Analysis service offers soft isolation for data storage.
ibm-lh data-copy --source-data-files s3://cust-bucket/warehouse/ --source-s3-creds "AWS_ACCESS_KEY_ID=xxxxxx,AWS_SECRET_ACCESS_KEY=yyyyyy,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --staging-location s3://cust-bucket/warehouse/staging/ --staging-s3-creds "AWS_ACCESS_KEY_ID=zzzzzz,AWS_SECRET_ACCESS_KEY=vvvvvvv,AWS_REGION=us-east-1,BUCKET_NAME=cust-bucket" --target-tables iceberg_target_catalog.ice_schema.cust_tab1 --ingestion-engine-endpoint "hostname=localhost,port=8080" --create-if-not-exist





 Ingest all Parquet files in a folder from S3 location by using a command 

To ingest all Parquet files in a folder from a S3 location, run the following command:

ibm-lh data-copy --source-data-files <SOURCE_DATA_FILE> --source-s3-creds "AWS_SECRET_ACCESS_KEY=<YOUR_SECRET_ASSESS_KEY>,AWS_ACCESS_KEY_ID=<YOUR_ACCESS_KEY_ID>,AWS_REGION=<YOUR_REGION>, BUCKET_NAME=<YOUR_BUCKET, ENDPOINT_URL=<YOUR_ENDPOINT_URL>>" --target-tables <TARGET_TABLES> --ingestion-engine-endpoint <INGESTION_ENGINE_ENDPOINT> --dbuser <DBUSER> --dbpassword <DBPASSWORD> --create-if-not-exist

For example:
Getting started with Netezza Performance Server 

IBM® Netezza® Performance Server for IBM Cloud Pak® for Data as a Service (Netezza Performance Server) is a fully managed, cloud native analytical data warehouse that is based on a massively parallel processing (MPP) architecture. Netezza Performance Server is mainly used for analytical processing of historical data. NPS can process from 100TB up to a few petabytes of data with best in the class performance. Its array of SPUs processes disjoint dataset in parallel which is then aggregated to come up with final result set.

Learn how to provision a Netezza Performance Server instance on Azure or AWS.



 Before you begin 

You need a pay-as-you-go IBM Cloud account or a subscription to start the provisioning process.

For more information, see [Setting up your IBM Cloud account](https://cloud.ibm.com/docs/account?topic=account-account-getting-started) and [Upgrading your account](https://cloud.ibm.com/docs/account?topic=account-upgrading-account).





 Provisioning Netezza Performance Server instances 



1. Go to the the [IBM Cloud catalog](https://cloud.ibm.com/catalog) or [Cloud Pak for Data as a Service](https://dataplatform.cloud.ibm.com/) web console.
2. Locate the Netezza Performance Server tile and click it.
You are redirected to the provisioning page.
3. Select your platform. You can choose Azure or AWS.
4. Choose a corresponding location. Based on your selection of platform, Netezza Performance Server is available in different locations.
5. Specify a name for your instance.
6. Choose a workload contour, Netezza units (NZU), and storage values. For more information about workload contours, Netezza Units (NZU), and available storage configuration, see [Architecture](https://cloud.ibm.com/docs/netezza?topic=netezza-compute-isolation&interface=ui).
7.
export dictionary PersonPositiveClues;

Example 2: Lemmatization

Consider a dictionary that has lemma match enabled and contains two entries: go shop and went shopping. The document contains the text Anna went shopping. The lemmatized form of the input document is Anna go shop. Lemma match returns went shopping as a match for the entry go shop. The original document text is not compared to the dictionary entries, only the lemmatized document text. Therefore, no matches exist in the document for the entry went shopping.





 Documenting the create dictionary and create external dictionary statements with AQL Doc 

The AQL Doc comment for a create dictionary statement contains the following information:

General description about the dictionary.

/
* A dictionary of terms used to greet people.
/

create dictionary GreetingDict as
(
'regards', 'regds', 'hello', 'hi', 'thanks', 'best', 'subj', 'to', 'from'
);

The AQL Doc comment for a create external dictionary statement contains the general description about the dictionary that is created. The following string illustrates the format:

/
* Customizable dictionary of given names.
* Evaluated on English, French, Italian, German, Portuguese, Spanish text.
/
create external dictionary CustomFirstNames_WesternEurope
allow_empty true;







 The create table statement 

The create table statement creates an AQL table.

The create table statement in AQL is used to define static lookup tables to augment annotations with more information.



 Syntax 

create table <table name> (
<colname> <type> [, <colname> <type>]* )
as values
( <value> [, <value>]),
...
( <value> [, <value>]);





 Description 



* <table name>

Specifies the name of the table to create. The <table name> can be a simple identifier or a double-quoted identifier.
* <colname>

Specifies the name of the column to create.
* <type>
ibmcloud watson-query virtualized-table-create --source-table-name table1 --source-table-def-file source_tabel_def.json --virtualized-schema DV_IBMID_270001PD8Q --sources CONN1:TABLE1 --virtualized-table-name TABLE1 --virtualized-table-def-file virtualized_table_def.json. The json file content example: "[{"column_name":"COL1","column_type":"VARCHAR"}]"







 ibmcloud watson-query virtualized-table-delete 

Remove specified virtualized table. You must specify the schema and table name.

ibmcloud watson-query virtualized-table-delete --virtualized-schema VIRTUALIZED-SCHEMA --virtualized-name VIRTUALIZED-NAME



 Command options 

--virtualized-schema (string)
: The schema of virtualized table to be deleted. Required.

--virtualized-name (string)
: The name of virtualized table to be deleted. Required.





 Examples 

Delete virtualized table

ibmcloud watson-query virtualized-table-delete --virtualized-schema DV_IBMID_270001PD8Q --virtualized-name TABLE1









 Primary catalog 

Manage the primary WKC catalog information in watson query console.



 ibmcloud watson-query primary-catalog 

Get primary catalog ID from the table.

ibmcloud watson-query primary-catalog



 Examples 

Get primary catalog ID from the table DVSYS.INSTANCE_INFO

ibmcloud watson-query primary-catalog







 ibmcloud watson-query primary-catalog-set 

Insert primary catalog ID into table DVSYS.INSTANCE_INFO.

ibmcloud watson-query primary-catalog-set --guid GUID



 Command options 

--guid (string)
: Primary catalog ID. Required.





 Examples 

Insert primary catalog ID into table DVSYS.INSTANCE_INFO