Learn more how to use eazyBI to analyze Jira Software projects. Analyze agile projects by sprints, epics, stories. Track sprint committed scope and track changes during the sprint. Burn-down and burn-up charts by story points and estimated hours.
How to Manage, Organize, and Visualize Projects with Profields Custom Fields ...eazyBI
How to add, manage, organize all the project specific-information fields you need: project status information, priority of projects, project due date, or total worked project hours. It is not possible in Jira, which is why we use Profields. Combine it with eazyBI for a comprehensive project visualization, overview, and management.
- David García, Product Manager - DEISER, Spain
Data-Driven Decisions in an Agile EnvironmenteazyBI
Building complex eazyBI reports might be difficult. What data should you pull into eazyBI? What calculations do you need? What might really be useful in reports? What wizardry is required to turn MDX into beautiful charts and graphs?
Team performance, project quality, team capacity and even funnel management, we give our teams all the information they need to focus their decisions on the right things.
- Walter Buggenhout, Atlassian Expert - ACA IT-Solutions NV, Belgium
Insight Asset Management in Jira and eazyBI Powered Insight ReportingeazyBI
Assets are all around us in our day to day work, whether it's IT assets, employees, customers, facilities or something else. How you can use Insight to help you manage assets within Jira and how eazyBI can provide flexible reports and an overview of your assets.
- Rickard Hyllenstam, Atlassian Consultant – Riada, Sweden
Management Visibility and Oversight in a Global R&D OrganisationeazyBI
How FinancialForce Engineering uses eazyBI and Adaptavist Test Management as part of our core infrastructure, to provide management visibility and oversight across a global R&D organization, with over 20 scrum teams.
- Paul Hardaker, Director of Development Operations, FinancialForce
How to leverage eazyBI for combined planning and finance reporting across the Tempo product suite.
- Kris Siwiec, Lead Consultant - New Verve Consulting, United Kingdom
How to Visualise, Understand, and Act on Salesforce Sales Data Using eazyBI?eazyBI
How we at Adaptavist use Salesforce, Jira, and eazyBI to improve the visibility of Sales and Sales pipeline for managers and the rest of the company.
- Rodrigo Molinare, Business Analyst, Adaptavist, UK
How eazyBI enabled Allegiant PMO to fully automate accounting reports and deliver them 100% on time! How eazyBI allows one to leverage multiple plug-in data sources via SQL and APIs.
- Pratik Patel, Traci Menga, and Bharath Kumar, the PMO Office, Allegiant Air & TridentSQA, USA, India
Learn the basics of eazyBI – import Jira standard and custom fields data, explore sample reports, create new reports and different charts types, create dashboards, embed eazyBI gadgets in Jira dashboards and Confluence pages.
How to Manage, Organize, and Visualize Projects with Profields Custom Fields ...eazyBI
How to add, manage, organize all the project specific-information fields you need: project status information, priority of projects, project due date, or total worked project hours. It is not possible in Jira, which is why we use Profields. Combine it with eazyBI for a comprehensive project visualization, overview, and management.
- David García, Product Manager - DEISER, Spain
Data-Driven Decisions in an Agile EnvironmenteazyBI
Building complex eazyBI reports might be difficult. What data should you pull into eazyBI? What calculations do you need? What might really be useful in reports? What wizardry is required to turn MDX into beautiful charts and graphs?
Team performance, project quality, team capacity and even funnel management, we give our teams all the information they need to focus their decisions on the right things.
- Walter Buggenhout, Atlassian Expert - ACA IT-Solutions NV, Belgium
Insight Asset Management in Jira and eazyBI Powered Insight ReportingeazyBI
Assets are all around us in our day to day work, whether it's IT assets, employees, customers, facilities or something else. How you can use Insight to help you manage assets within Jira and how eazyBI can provide flexible reports and an overview of your assets.
- Rickard Hyllenstam, Atlassian Consultant – Riada, Sweden
Management Visibility and Oversight in a Global R&D OrganisationeazyBI
How FinancialForce Engineering uses eazyBI and Adaptavist Test Management as part of our core infrastructure, to provide management visibility and oversight across a global R&D organization, with over 20 scrum teams.
- Paul Hardaker, Director of Development Operations, FinancialForce
How to leverage eazyBI for combined planning and finance reporting across the Tempo product suite.
- Kris Siwiec, Lead Consultant - New Verve Consulting, United Kingdom
How to Visualise, Understand, and Act on Salesforce Sales Data Using eazyBI?eazyBI
How we at Adaptavist use Salesforce, Jira, and eazyBI to improve the visibility of Sales and Sales pipeline for managers and the rest of the company.
- Rodrigo Molinare, Business Analyst, Adaptavist, UK
How eazyBI enabled Allegiant PMO to fully automate accounting reports and deliver them 100% on time! How eazyBI allows one to leverage multiple plug-in data sources via SQL and APIs.
- Pratik Patel, Traci Menga, and Bharath Kumar, the PMO Office, Allegiant Air & TridentSQA, USA, India
Learn the basics of eazyBI – import Jira standard and custom fields data, explore sample reports, create new reports and different charts types, create dashboards, embed eazyBI gadgets in Jira dashboards and Confluence pages.
Explore how to analyze Jira Service Desk projects with eazyBI. Analyze created and resolved issues, resolution time, the age of open issues. Analyze any defined SLA, track SLA breaches, SLA cycle time, drill through to individual issues.
Learn about eazyBI integrations with popular Jira test management add-ons (Xray, Zephyr, Adaptavist). Track test execution status, requirements coverage, identified defects.
Discover how to use eazyBI for planning and budgeting. Define additional issue hierarchies for data aggregation. Import other financial data for budgeting. Current workarounds and future planned integrations with other planning and budgeting add-ons (Jira Portfolio, Tempo Budgets).
Import and analyze data from other data sources. Analyze Git commit logs, import data from REST API and SQL sources. Transform REST API data with JavaScript before importing into eazyBI.
* Advanced eazyBI settings for additional custom field types
- JIRA Misc Custom Fields
- JavaScript calculated custom fields
* eazyBI Import from other data sources
* Calculated member formulas
- JIRA Issues change
- JIRA Issues created from total %
- JIRA Issues created from parent %
- JIRA Bugs created
- JIRA Bugs created %
- JIRA Issues created in previous period
- JIRA Issues created change %
- JIRA Cumulative issues created
- JIRA Cumulative issues resolved
- JIRA Cumulative issues resolved trend
- JIRA Issues created avg last 3 months
- Hours cost
* Calculated members in other dimensions
- Transition Status
* eazyBI data model
- How eazyBI works
- JIRA Transactional data
- Multi-dimensional OLAP cubes
- Dimensions, measures, hierarchies, levels and members
- JIRA data import
* eazyBI Accounts, cubes, reports, and dashboards
* eazyBI shared JVM/Child process
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Explore how to analyze Jira Service Desk projects with eazyBI. Analyze created and resolved issues, resolution time, the age of open issues. Analyze any defined SLA, track SLA breaches, SLA cycle time, drill through to individual issues.
Learn about eazyBI integrations with popular Jira test management add-ons (Xray, Zephyr, Adaptavist). Track test execution status, requirements coverage, identified defects.
Discover how to use eazyBI for planning and budgeting. Define additional issue hierarchies for data aggregation. Import other financial data for budgeting. Current workarounds and future planned integrations with other planning and budgeting add-ons (Jira Portfolio, Tempo Budgets).
Import and analyze data from other data sources. Analyze Git commit logs, import data from REST API and SQL sources. Transform REST API data with JavaScript before importing into eazyBI.
* Advanced eazyBI settings for additional custom field types
- JIRA Misc Custom Fields
- JavaScript calculated custom fields
* eazyBI Import from other data sources
* Calculated member formulas
- JIRA Issues change
- JIRA Issues created from total %
- JIRA Issues created from parent %
- JIRA Bugs created
- JIRA Bugs created %
- JIRA Issues created in previous period
- JIRA Issues created change %
- JIRA Cumulative issues created
- JIRA Cumulative issues resolved
- JIRA Cumulative issues resolved trend
- JIRA Issues created avg last 3 months
- Hours cost
* Calculated members in other dimensions
- Transition Status
* eazyBI data model
- How eazyBI works
- JIRA Transactional data
- Multi-dimensional OLAP cubes
- Dimensions, measures, hierarchies, levels and members
- JIRA data import
* eazyBI Accounts, cubes, reports, and dashboards
* eazyBI shared JVM/Child process
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.