Adjudication of dispute regarding RERC (Forecasting, Scheduling, Deviation Se...Das A. K.
Adjudication of dispute regarding RERC (Forecasting, Scheduling, Deviation Settlement and related matters of Solar and Wind Generation Sources) Regulations, 2017
MP_SLDC_RE_DSM_from-01-01-2019-to-31-01-2019_dailyDas A. K.
DSM Account of Wind and Solar Pooling Stations for the month of January, 2019 on the basis of MPERC
(Forecasting, Scheduling, Deviation Settlement Mechanism and related matters of Wind and Solar Generating
Stations) Regulations, 2018.
normalized parametric model for wind turbine power curveDas A. K.
Power curve model of Wind Turbine is one of the basic building blocks of Wind Power forecasting methodology. Without a proper power curve model, only AI based data driven forecast can not work. The recent article on power curve model can be useful to those who are in active research in wind power forecast.
Mock RE - Gujarat State Deviation Settlement Account (Deviation charges)Das A. K.
Mock RE -Gujarat State Deviation Settlement Account (Deviation charges)
[https://sldcguj.com/Commercial/uploaded_mock/MOCK_RE-DSM_WK49_INTER_SOLAR_PAYABLE.pdf]
Adjudication of dispute regarding RERC (Forecasting, Scheduling, Deviation Se...Das A. K.
Adjudication of dispute regarding RERC (Forecasting, Scheduling, Deviation Settlement and related matters of Solar and Wind Generation Sources) Regulations, 2017
MP_SLDC_RE_DSM_from-01-01-2019-to-31-01-2019_dailyDas A. K.
DSM Account of Wind and Solar Pooling Stations for the month of January, 2019 on the basis of MPERC
(Forecasting, Scheduling, Deviation Settlement Mechanism and related matters of Wind and Solar Generating
Stations) Regulations, 2018.
normalized parametric model for wind turbine power curveDas A. K.
Power curve model of Wind Turbine is one of the basic building blocks of Wind Power forecasting methodology. Without a proper power curve model, only AI based data driven forecast can not work. The recent article on power curve model can be useful to those who are in active research in wind power forecast.
Mock RE - Gujarat State Deviation Settlement Account (Deviation charges)Das A. K.
Mock RE -Gujarat State Deviation Settlement Account (Deviation charges)
[https://sldcguj.com/Commercial/uploaded_mock/MOCK_RE-DSM_WK49_INTER_SOLAR_PAYABLE.pdf]
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
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.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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.
