"By the time peer review completes, this project will have produced over half a dozen research papers in just two years. We chronologically summarise below the two most important results from each year.
# Vectorised phrases
A challenge with analysing text is that words alone often do not provide sufficient context. For example, the word ""mount"" could refer to boarding a horse or any number of mountains in the English-speaking world. However, most text applications that process text simply convert every word into a unique numeric representation, and the context is lost. We devised a method for encoding variable-length phrases into just 128 bits (i.e. the length of a _narrow_ vector register) and an algorithm to mine the variable-length phrases with just a short sequence of instruction-parallel operations, using the vector registers to process up to four possible phrases simultaneously.
# Clipped bounding boxes
In collaboration with Human Brain Project researchers in Switzerland, we devised a method to accelerate classic spatial search. In general, objects are represented with the smallest (hyper-)rectangle that encloses them, because of the simplicity and efficiency of this representation. However, by focusing only on the corners of these _bounding boxes_, one can ""clip away"" excess space with minimal representation overhead. This tighter approximation of the underlying content improves query performance, because it prevents falsely concluding that an object lies in the empty corners of the bounding boxes. As a fundamental improvement to basic spatial search, this implies immediate improvements to spatio-temporal-textual search, too.
# Vectorised trees
The R-tree (as its well-known variants) is a classic data structure for multi-dimensional spatial search. It groups together nearby objects and represents the entire group with their approximating (clippable!) bounding box. Classically, at query time, each element of these groups is processed one-by-one; however, with ""padding"" it is possible to process up to 8 bounding boxes simultaneously by employing auto-vectorisation on machines with 512-bit vector (a.k.a. SIMD) registers. We showed through extensive experiments that, irrespective of the variant of R-tree chosen, significant query time improvements can be made in this fashion.
# Decaying temporality
It is an eternal question, whether time is fundamentally different from space. Yet most queries treat space as more general: it is enough to find the nearest spatial objects, but temporal boundaries are taken as fixed and rigid. We demonstrated that treating time as a ""decaying"" concept, that something new is more interesting, but something old isn't ""out of boundary,"" can identify more interesting textual concepts from a corpus of text than treating time as a (clippable!) bounding box."
