Practically all of the planning research is limited to states represented in terms of Boolean and numeric state variables. Many practical problems, for example, planning inside complex software systems, require far more complex data types, and even real-world planning in many cases requires concepts such as sets of objects, which are not convenient to express in modeling languages with scalar types only. In this work, we investigate a modeling language for complex software systems, which supports complex data types such as sets, arrays, records, and unions. We give a reduction of a broad range of complex data types and their operations to Boolean logic, and then map this representation further to PDDL to be used with domain-independent PDDL planners. We evaluate the practicality of this approach, and provide solutions to some of the issues that arise in the PDDL translation.

Data simulation is fundamental for machine learning and causal inference, as it allows exploration of scenarios and assessment of methods in settings with full control of ground truth. Directed acyclic graphs (DAGs) are well established for encoding the dependence structure over a collection of variables in both inference and simulation settings. However, while modern machine learning is applied to data of an increasingly complex nature, DAG-based simulation frameworks are still confined to settings with relatively simple variable types and functional forms. We here present DagSim, a Python-based framework for DAG-based data simulation without any constraints on variable types or functional relations. A succinct YAML format for defining the simulation model structure promotes transparency, while separate user-provided functions for generating each variable based on its parents ensure simulation code modularization. We illustrate the capabilities of DagSim through use cases where metadata variables control shapes in an image and patterns in bio-sequences.

"Supporting arrays, maps and structs allows customer to simplify data pipelines, unify more of their semi-structured data with their data warehouse as well as maintain better real world representation of their data from relationships between entities to customer orders with item level detail. A good example is groups of cell phone towers that are used for one call while driving on the highway." I have interviewed Mark Lyons, Director of Product Management at Vertica. We talked about the new Vertica 10.0 What is your role at Vertica? Mark Lyons: My role at Vertica is Director of Product Management. I have a team of 5 product managers covering analytics, security, storage integrations and cloud.

Automatic Machine Learning (aka AutoML) has been gaining traction within the Data Science community. This surge of interest is reflected on the development and release of numerous open source AutoML libraries (e.g., AutoWeka, MLBox, auto-sklearn, TPOT, HpBandSter, AutoKeras, prophet), and on the emergence of businesses focused on building and commercialising AutoML systems (e.g., DataRobot, DarwinAI, H2O.ai, OneClick.ai). Although AutoML is a hot topic and many articles are being written about it (e.g., H2O.ai's Erin LeDell, Fast.ai's Rachel Thomas, and KDnuggets' Matthew Mayo), just a few have emphasized and clarified the limitations of current AutoML systems. It is our intention to address this gap by pointing out what we believe to be AutoML's main drawbacks currently.