Large-scale data analytics using statistical machine learning (ML), popularly called
advanced analytics, underpins many modern data-driven applications. The data …

Summary The Internet of Things (IoT) is an emerging technology paradigm where millions of
sensors and actuators help monitor and manage physical, environmental, and human …

In the modern era, digital data processing with a huge volume of data from the repository is
challenging due to various data formats and the extraction techniques available. The …

Configuration tuning is vital to optimize the performance of big data analysis platforms like
Spark. Existing methods (eg auto-tuning relational databases) are not effective for tuning …

In a fast-changing data-driven world, real-time data processing systems are becoming
ubiquitous in everyday applications. The increasing data we produce, such as audio, video …

Abstract Internet of Things (IoT) is a technology paradigm where millions of sensors monitor,
and help inform or manage, physical, environmental and human systems in real-time. The …

Data science pipelines are typically exploratory. An integral task of such pipelines are
feature transformations, which transform raw data into numerical matrices or tensors for …

Spark has been increasingly employed by industries for big data analytics recently, due to its
resilience, scalability and efficient in-memory distributed programming model. Meanwhile …

Benchmarking Big Data solutions has been gaining a lot of attention from research and
industry. BigBench is one of the most popular benchmarks in this area which was adopted …

Declarative machine learning (ML) aims at the high-level specification of ML tasks or
algorithms, and automatic generation of optimized execution plans from these specifications …