With the ongoing increasing amount of data, these data have to be processed to gain new insights. Data mining techniques and user-driven OLAP are used to identify patterns or rules. Typical OLAP queries require database operations such as selections on ranges or projections. Similarly, data mining techniques require efficient support of these operations. One particularly challenging, yet important property, that an efficient data access has to support is multi-dimensionality. New techniques have been developed taking advantage of novel hardware environments including SIMD or main-memory usage. This includes sequential data access methods such SIMD, BitWeaving, or Column Imprints. New data structures have been also developed, including Sorted Projections or Elf, to address the features of modern hardware and multi-dimensional data access. In the context of multidimensional data access, the influence of modern hardware, including main-memory data access and SIMD instructions lead to new data access techniques. This chapter gives an overview on existing techniques and open potentials.
High performance cache management for sequential data access