Space, time, sensors, and data semantics

ABSTRACT

In this talk we will discuss the issues concerning data management for environmental monitoring of moving phenomena in continuous media. Specifically, we will concentrate on three topics: non-Newtonian notions of time, measurement units, and the need for better spatio-temporal data models, such as fiber bundle data models to model vector field data.

Classical temporal data modeling for databases has invoked a Newtonian conception of time, with the notion of universal simultaneous time. Einstein's theory of relativity has supplanted the earlier Newtonian model of time among physicists, astronomers, and now GPS users. We will discuss why and how this matters for DBMS systems.

Most contemporary DBMS systems, query languages, etc. entirely ignore issues of measurement units failing to adequately support many sensor based applications. We will discuss some measurement unit issues and dimensional analysis and suggest that they be incorporated into our type systems for DBMSs.

We note that conventional DBMSs (relational, object oriented, OLAP, and XML) are built from collections of discrete things (tuples, objects, "facts", or trees). However, for many applications, such as weather forecasting, climate simulations, oceanography, water pollution studies, astrophysics, and other fluid dynamics applications, such collections of discrete objects (e.g., sets) are not an appropriate data model. Common to many of these applications is the notion of vector field data (such as velocity fields for wind or ocean currents). It hardly makes sense to talk about interpolation in classical data models of collections of discrete objects. We will recount some of the basic ideas of fiber bundle data models, first investigated by Lloyd Treinish at IBM for data visualization applications. Variations of such data models have sometimes been referred to as vector bundle data models or as sheaf data models.

We summarize by suggesting that sensor data management for environmental monitoring of fluid dynamics phenomena (weather, climate, oceanography, etc.) is an area of growing importance, rapidly growing data sets, and in need of considerable development of better data management technology. We will then discuss a number of funding opportunities at National Science Foundation that support such research.

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.4 Systems

Additional Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications

  J. Computer Applications
  J.2 PHYSICAL SCIENCES AND ENGINEERING
      Subjects: Earth and atmospheric sciences

General Terms:
Design