Datalog is a deductive query language for relational databases. We introduce LogiQL, a language based on Datalog and show how it can be used to specify mixedinteger linear optimization models and solve them. Unlike pure algebraic modeling languages, LogiQL allows the user to both specify models, and manipulate and transform the inputs and outputs of the models. This is an advantage over conventional optimization modeling languages that rely on reading data via plug-in tools or importing data from external sources via files. In this chapter, we give a brief overview of LogiQL and describe two mixed integer programming case studies: a production-transportation model and a formulation of the traveling salesman problem.

The standard process of data science tasks is to prepare features inside a database, export them as a denormalized data frame and then apply machine learning algorithms. This process is not optimal for two reasons. First, it requires denormalization of the database that can convert a small data problem into a big data problem. The second shortcoming is that it assumes that the machine learning algorithm is disentangled from the relational model of the problem. That seems to be a serious limitation since the relational model contains very valuable domain expertise. In this paper we explore the use of convex optimization and specifically linear programming, for modelling machine learning algorithms on relational data in an integrated way with data processing operators. We are using SolverBlox, a framework that accepts as an input Datalog code and feeds it into a linear programming solver. We demonstrate the expression of common machine learning algorithms and present use case scenarios where combining data processing with modelling of optimization problems inside a database offers significant advantages.

The main aim of the present study was to investigate the effects of soccer-specific training on physical fitness components in adolescent elite soccer players and make comparisons with older counterparts. Twenty two male soccer players from the Serbian First Division team were allocated to two assigned trials according to age – young group (YG) and mature group (MG). Players in their teenage years (19 years and younger) were assigned to YG (10 subjects) and others to MG (12 subjects). Between the first and second test session, all subjects followed six weeks of soccer-specific periodized training programme. There were no differences between groups at preand post-training trial for body mass, vertical jump height, average anaerobic power and VO2max (P>0.05). Body fat was significantly lower in YG before and after training program as compared to MG (P<0.05). Body mass and fat dropped significantly in both groups after training program (P<0.05). Furthermore, average anaerobic power and VO2max along with vertical jump height, were significantly improved in both groups (P<0.05) at posttraining performance. Finally, the magnitude of change in VO2max was significanty superior in MG as compared to YG after training program (18.3 vs. 7.8%; P<0.05). The findings of the present study indicate that the trainability indices are not highly influenced by age in top-level soccer players. (Biol.Sport 26:379-387, 2009)