crucial to fit data in memory  automatic lossless compression  Sub-problems: #rows>>#cols, column correlation, column characteristics  column-wise compression w/ heterogeneous encoding formats Time Prog- Linear Algebra  Data characteristics inspired overall design of encoding schemes  Initially slow compression  dedicated sampling schemes and estimators  Initially slow compressed operations  cache-conscious it's an interesting problem General-purpose, heavy- and lightweight compression techniques struggle to achieve both good compression ratios and fast decompression speed to enable block-wise uncompressed

the style guide, the following C++11 features may not be used: • Compile-time rational numbers (<ratio>), because of concerns that it’s tied to a more template-heavy interface style. • The and

Relevant Work  Motivation  Some research areas might be very large (e.g., index structures, compression)  How do you find relevant scientific papers/thesis via multiple channels  By Venue/Year  particularly poor. We use index structures like b-trees, tries, and hash tables; as well as compression techniques like run-length encoding, dictionary encoding, and null suppression. A woman without Figure~\ref{fig:exp1} Equation~\eqref{eq:e1} employ general-purpose compression techniques employ a general-purpose compression technique Each entry ???????????????????????? can be expressed over

characteristics  Systematic evaluation w/ datasize, sparsity, etc  Inappropriate for certain topics: compression, ML accuracy  “Real” Data Repositories  Wide selection of available datasets w/ different characteristics