This document provides SQL queries to extract fast formula related data from underlying tables, including:
1) A query that retrieves fast formula functions and their underlying packages.
2) A query that lists fast formulas with their FF code, description, edit status, formula text and other metadata.
3) A query that extracts database items (DBIs) from a process order, including the module, group, user name, data type, definition and contexts used.
4) A query that retrieves fast formula contexts by formula type.
This is very helpful technical guide to learn the complete process of Payroll in Oracle HRMS pertaining to following steps,
1. Running the Payroll and Reviewing the Results
2. Running the Prepayments,
3. Running the Costing,
4. Transferring the Payroll to General Ledger (GL)
5. Importing the Journals and Posting them
Thanks,
Faisal Anwar
firstfaisal@yahoo.com
+971 555749650
+92 335 0979700
What SQL functionality was added in the past year or so. The presentation covers default expressions, functional key parts, lateral derived tables, CHECK constraints, JSON and spatial improvements. Also some other small SQL and other improvements.
This is very helpful technical guide to learn the complete process of Payroll in Oracle HRMS pertaining to following steps,
1. Running the Payroll and Reviewing the Results
2. Running the Prepayments,
3. Running the Costing,
4. Transferring the Payroll to General Ledger (GL)
5. Importing the Journals and Posting them
Thanks,
Faisal Anwar
firstfaisal@yahoo.com
+971 555749650
+92 335 0979700
What SQL functionality was added in the past year or so. The presentation covers default expressions, functional key parts, lateral derived tables, CHECK constraints, JSON and spatial improvements. Also some other small SQL and other improvements.
The Perforce Web Content Management System development team, lacking a pre-existing solution in PHP, designed and implemented their own object model and record layer to ease the interaction of the system with the Perforce Server. This session will focus on how users can access files in Perforce via a simple CRUD API, the subsystems exposed, and their usage.
Modify this code to change the underlying data structure to .pdfadityaenterprise32
Modify this code to change the underlying data structure to a double ended doubly-linked list. Data
records should be added to the end of the main array (the database) and three double ended
doubly linked lists should be maintained ; one each for the ID, LastName and FirstName. The
linked lists should (of course) be maintained in order You MUST use the driver program I have
provided , and make some additions. Remember, you should NOT be permitted to add a record
with a duplicate index number. You MUST implement all methods as I have indicated. Printing out
the data in forward order should use the "next" link in each node, printing out in reverse order
should employ the "prev" link.
import java.io.File;
import java.io.FileNotFoundException;
import java.util.Scanner;
public class DataBase {
private DataBaseArray dbArr;
private IndexArray firstIA, lastIA, IDIA;
private DeleteStack deleteStack;
private Scanner scan;
private String nld = "n-----------------------------n";
private String dnl = "-----------------------------n";
//Default Constructor
public DataBase() {
this.dbArr = new DataBaseArray(100);
this.firstIA = new IndexArray(100, false);
this.lastIA = new IndexArray(100, false);
this.IDIA = new IndexArray(100, true);
this.deleteStack = new DeleteStack(100);
readInDataFromFile();
scan = new Scanner(System.in);
}
//Constructor for custom size
public DataBase(int maxSize) {
this.dbArr = new DataBaseArray(maxSize);
this.firstIA = new IndexArray(maxSize, false);
this.lastIA = new IndexArray(maxSize, false);
this.IDIA = new IndexArray(maxSize, true);
this.deleteStack = new DeleteStack(maxSize);
readInDataFromFile();
scan = new Scanner(System.in);
}
//Read in the initial records
public void readInDataFromFile() {
File dbData = new File("dbData.txt");
Scanner scan = new Scanner(System.in); //Have to initialize to something for compilation
try {
scan = new Scanner(dbData);
} catch (FileNotFoundException e) {
e.printStackTrace();
}
String ID = "";
String fname = "";
String lname = "";
String[] values;
while(scan.hasNextLine()) {
values = scan.nextLine().split(",");
lname = values[0];
fname = values[1];
ID = values[2];
this.insertIt(ID, fname, lname);
}
}
//Ask user to specify ID, delete that record
public void deleteIt() {
String ID = "";
System.out.println("nDELETING" + nld);
System.out.println("Please enter the ID of the record to be deleted: ");
ID = this.scan.nextLine();
if (this.IDIA.searchByKey(ID) == -1) {
System.out.println(dnl + "Record not found, please try again." + nld);
return;
}
this.delete(ID);
System.out.println(dnl + "Record successfully deleted" + nld);
}
//Ask user to specify ID, return data of that record
public void findIt() {
String ID = "";
System.out.println("nFINDING" + nld);
System.out.println("Please enter the ID of the record to be found: ");
ID = this.scan.nextLine();
int iaIndexOfRecord = this.IDIA.searchByKey(ID);
if (iaIndexOfRecord == -1) {
System.out.println(dnl + "Record not found, please try again." + nld);
return.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Fast formula queries for functions, contexts, db is and packages
1. Fast Formula Queries for Functions, Contexts, DBIs and Packages
Beloware the SQL's to extractfast formularelateddatafromunderlyingtables:
Fast Formula Functionsand theirunderlyingPackages:
selectf.BASE_FUNCTION_NAME,f.description, f.definition||'('||
listagg(fp.name||''||decode(fp.class,'I','IN','O','OUT')||'
'||decode(fp.data_type,'N','NUMBER','T','VARCHAR2','D','DATE'),',') WITHIN GROUP(ORDER BY
fp.sequence_number)
||') RETURN '||decode(f.data_type,'N','NUMBER','T','VARCHAR2','D','DATE') ASfunctionCall
fromff_function_parametersfp
, FF_FUNCTIONS_VLf
where f.function_id=fp.function_id
andupper(f.description) notlike '%DEPRECATED%'
groupby f.BASE_FUNCTION_NAME,f.description,f.definition,f.data_type
Fast Formula'sList with FF Code:
select TO_CHAR("FF_FORMULAS_VL"."EFFECTIVE_START_DATE",'DD-MON-YYYY') as
"EFFECTIVE_START_DATE",
TO_CHAR("FF_FORMULAS_VL"."EFFECTIVE_END_DATE",'DD-MON-YYYY') as"EFFECTIVE_END_DATE",
"FF_FORMULAS_VL"."BASE_FORMULA_NAME"as"BASE_FORMULA_NAME",
"FF_FORMULAS_VL"."FORMULA_NAME"as "FORMULA_NAME",
"FF_FORMULAS_VL"."DESCRIPTION"as"DESCRIPTION",
"FF_FORMULAS_VL"."EDIT_STATUS"as "EDIT_STATUS",
"FF_FORMULAS_VL"."FORMULA_TEXT"as "FORMULA_TEXT",
"FF_FORMULAS_VL"."COMPILE_FLAG"as"COMPILE_FLAG",
"FF_FORMULAS_VL"."LEGISLATION_CODE"as"LEGISLATION_CODE",
TO_CHAR("FF_FORMULAS_VL"."LAST_UPDATE_DATE",'DD-MON-YYYY') as"LAST_UPDATE_DATE",
"FF_FORMULAS_VL"."LAST_UPDATED_BY"as "LAST_UPDATED_BY",
"FF_FORMULAS_VL"."CREATED_BY"as "CREATED_BY",
TO_CHAR("FF_FORMULAS_VL"."CREATION_DATE",'DD-MON-YYYY') as"CREATION_DATE",
"FF_FORMULA_TYPES_TL"."FORMULA_TYPE_NAME" as "FORMULA_TYPE_NAME"
from "FUSION"."FF_FORMULA_TYPES_TL""FF_FORMULA_TYPES_TL",
"FUSION"."FF_FORMULAS_VL""FF_FORMULAS_VL"
where "FF_FORMULAS_VL"."FORMULA_TYPE_ID"="FF_FORMULA_TYPES_TL"."FORMULA_TYPE_ID"
Extracting DBIs from a POD:
selectfat.module_name,fdg.base_group_name,fdg.group_name,fdi.base_user_name,fdi.user_name,
fdi.description,fdi.data_type,fdi.definition_text,
fue.base_user_entity_name,fue.descriptionfue_des,fr.base_route_name,fr.multi_row_flag,
(selectsubstr(sys.stragg(','||base_context_name),2) context
from fusion.ff_route_context_usagesi,fusion.ff_contexts_vl j
where i.context_id=j.context_id
2. and i.route_id=fr.route_id) contexts_used,
(selectsubstr(sys.stragg(','||parameter_name),2) context
fromfusion.ff_route_parameters
where route_id=fr.route_id) parameters,
(selectsubstr(sys.stragg(','||base_context_name),2) context
from fusion.ff_dbi_groups_vl a,fusion.ff_dbi_usagesb,fusion.ff_database_items_vlc,
fusion.ff_contexts_vld
where a.context_id=d.context_id
and a.dbi_group_id= b.dbi_group_id
and b.dbi_id= c.database_item_id
and c.user_entity_id=fue.user_entity_id) contexts_set
from fusion.ff_database_items_vl fdi,fusion.ff_dbi_usagesfdu,fusion.ff_dbi_groups_vl fdg,
fusion.fnd_appl_taxonomy_vlfat,
fusion.ff_user_entities_vlfue,fusion.ff_routes_vl fr
where fdi.module_idisnotnull
and fdi.database_item_id=fdu.dbi_id(+)
and fdu.dbi_group_id=fdg.dbi_group_id(+)
and fdi.module_id =fat.module_id
and fdi.user_entity_id=fue.user_entity_id
andfue.route_id=fr.route_id
and fdi.module_idisnotnull
orderby module_name,fdi.base_user_name
Fast Formula Contextsby Formula Type:
selectt.base_formula_type_name
, ttl.formula_type_name
, ttl.description
, c.base_context_name
from ff_formula_types_bt
, ff_formula_types_tl ttl
, ff_ftype_context_usagesu
, ff_contexts_bc
where t.formula_type_id=u.formula_type_id
and ttl.formula_type_id=t.formula_type_id
and ttl.language =userenv('LANG')
/*and ttl.formula_type_name like 'Oracle%Payroll%'*/
and c.context_id=u.context_id
Hope these querieshelp.
Stay tunedformore updates.