This open access book provides an overview of state-of-the-art studies on a new paradigm “Sublinear Computation Paradigm” suggested by the large multiyear academic research project in Japan “Foundations of Innovative Algorithms for Big Data.” In our rapidly evolving “age of big data,” massive increases in big data create many novel and uncharted opportunities. To handle unprecedented explosions in big data sets in research, industry and all areas of our society there is an urgent need to develop novel methods and approaches for big data analysis. To deal with this urgency, we are demanding innovative changes to the algorithm theory for the big data. For example, previously we have considered that polynomial time algorithms are “fast,” but if we apply an O¹n2 º-time algorithm for the big data with peta byte scale or more, we would encounter computational resources or running time problems. To deal with this critical computational and algorithmic bottle neck, we require linear, sublinear, or constant time algorithms. We created a foundation of innovative algorithms by developing algorithms, data structures, and modeling techniques for the big data. The project is organized by three sub-teams focusing on sublinear algorithms, sublinear data structures and sublinear modeling, respectively. This book consists of five parts: Part 0, which consists of only one chapter, provides the concept of the sublinear computation paradigm; Parts 1, 2, and 3 review results on sublinear algorithms, sublinear data structures, and sublinear modeling, respectively; Part 4 shows some application results. weiterlesen