BACKGROUND

Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.

The evolving increase in size and scale of various types of complex systems, can require the participation of a number of various individuals with different skill sets, knowledge, and experience. For example, a reliability engineer be called upon to diagnose and correct problems arising from the faulty operation of one or more system components spread across a number of different physical sites, in order to keep a system up and running.

The reliability engineer may in turn provide instructions to one or more work center expert(s) dedicated to a particular worksite. The skill set and institutional knowledge of that work center expert may be more specialized, reflecting the narrower focus of his or her vocational duties.

Still other individuals may be responsible for planning operation of the complex system. Such planners may focus upon larger issues of supply chain management of essential components, and forecasting demands expected to be made upon the system in the near term, or the projected future.

Such complex operational environments can make it difficult to efficiently share accurate, updated information between different individuals. Moreover, the sheer volume of information relating to the complex system can be overwhelming, clouding the ability to accurately diagnose problems, and to respond quickly to correct those problems.

SUMMARY

Embodiments relate to (global, lateral) data filtering techniques which may be employed alone or in combination. A global filter allows data filtering using attributes of a data object across multiple Analysis Tools (ATs), through the broadcast of semantic filter context objects. Upon selecting attribute values as part of the global query, the filter context object with attribute data is created with attribute names and values. The filter context object is then resolved into a data object, and subsequently into target data by a processing engine. Passing the filter context object (rather than already-resolved Entity IDs) to the processing engine, reduces the payload and renders communication between services fast and efficient.

A lateral filter finds related entities in a relational database, without having to maintain and/or duplicate all of the data into a graph database. The processing engine resolves lateral filters using an entity graph path calculation performed in conjunction with the generation of a graph structure bootstrapped from available information. The bootstrapped graph structure is constructed utilizing available semantic data regarding database schematics (schemantics). These may be a combination of pre-calculated—e.g., Primary Key (PK)/Foreign Key (FK)—relations between database tables, as well as other metadata read from the relational database. From that information, relationships in the bootstrapped graph structure are created. Possible paths taken between entities in the bootstrapped graph structure are used to generate an optimized SQL, query to reach the target data. The bootstrapped graph can change dynamically based upon received schemantics, thereby desirably reducing a size of the bootstrapped graph structure. Such a dynamic nature can be particularly beneficial when the bootstrapped graph structure spans multiple databases.

The following detailed description and accompanying drawings provide a better understanding of the nature and advantages of various embodiments.

