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Articles by Ilya Katsov
Digital assistants are the future of banking
Grid Dynamics The banking industry is in the middle of a significant transformation. Fintech startups and other non-traditional companies are gaining market share at the cost of traditional banks, and customer attrition is at an all time high. Source: CB INSIGHTS According to Boston Consulting Group, corporate banks lose between 10–15% of gross revenues due to attrition annually — there was a 15% attrition rate amongst retail financial institutions per year (Source: Boston Consulting Group).
5 Trends in Supply Chain Analytics and Optimization in 2021
Here are several trends that should be on the radar of companies that develop in-house supply chain optimization models or make industrial supply chain management software. Statistical modeling and mathematical optimization play a central role in supply chain management because of the large number of micro-decisions that need to be made and the necessity to plan operations in advance in the face of uncertainty.
5 Trends in Supply Chain Analytics and Optimization in 2021
Statistical modeling and mathematical optimization play a central role in supply chain management because of the large number of micro-decisions that need to be made and the necessity to plan operations in advance in the face of uncertainty. Here are several trends that should be on the radar of companies that develop in-house supply chain optimization models or make industrial supply chain management software. 1.
Preparing Supply Chain Optimization Models for COVID-19 Vaccination Period
The Coronavirus disease (COVID-19) pandemic has created multiple waves of disruption for retail and manufacturing supply chains because of repeated cycles of lockdown and reopening. The rollout of COVID-19 vaccines, which marks the beginning of the biggest vaccination campaign in history, means a new series of supply chain challenges and adds more uncertainty on top of the lockdown-and-reopen disruptions, which are partially understood and controlled by this time.
MapReduce Patterns, Algorithms, and Use Cases
In this article I digested a number of MapReduce patterns and algorithms to give a systematic view of the different techniques that can be found on the web or scientific articles. Several practical case studies are also provided. All descriptions and code snippets use the standard Hadoop’s MapReduce model with Mappers, Reduces, Combiners, Partitioners, and sorting. This framework is depicted in the figure below.
Probabilistic Data Structures for Web Analytics and Data Mining
Statistical analysis and mining of huge multi-terabyte data sets is a common task nowadays, especially in the areas like web analytics and Internet advertising. Analysis of such large data sets often requires powerful distributed data stores like Hadoop and heavy data processing with techniques like MapReduce. This approach often leads to heavyweight high-latency analytical processes and poor applicability to realtime use cases.
Deep reinforcement learning for supply chain and price optimization
Grid Dynamics Supply chain and price management were among the first areas of enterprise operations that adopted data science and combinatorial optimization methods and have a long history of using these techniques with great success.
In-Stream Big Data Processing
The shortcomings and drawbacks of batch-oriented data processing were widely recognized by the Big Data community quite a long time ago. It became clear that real-time query processing and in-stream processing is the immediate need in many practical applications. In recent years, this idea got a lot of traction and a whole bunch of solutions like Twitter’s Storm, Yahoo’s S4, Cloudera’s Impala, Apache Spark, and Apache Tez appeared and joined the army of Big Data and NoSQL systems.
NoSQL Data Modeling Techniques
NoSQL databases are often compared by various non-functional criteria, such as scalability, performance, and consistency. This aspect of NoSQL is well-studied both in practice and theory because specific non-functional properties are often the main justification for NoSQL usage and fundamental results on distributed systems like the CAP theorem apply well to NoSQL systems. At the same time, NoSQL data modeling is not so well studied and lacks the systematic theory found in relational databases.
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