Good lecture notes on the current state of the art in data structure research.

Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structures:

TIME TRAVEL We can remember the past efficiently (a technique called persistence), but in general it's difficult to change the past and see the outcomes on the present (retroactivity). So alas, Back To The Future isn't really possible.
GEOMETRY When data has more than one dimension (e.g. maps, database tables).
DYNAMIC OPTIMALITY Is there one binary search tree that's as good as all others? We still don't know, but we're close.
MEMORY HIERARCHY Real computers have multiple levels of caches. We can optimize the number of cache misses, often without even knowing the size of the cache.
HASHING Hashing is the most used data structure in computer science. And it's still an active area of research.
INTEGERS Logarithmic time is too easy. By careful analysis of the information you're dealing with, you can often reduce the operation times substantially, sometimes even to constant. We will also cover lower bounds that illustrate when this is not possible.
DYNAMIC GRAPHS A network link went down, or you just added or deleted a friend in a social network. We can still maintain essential information about the connectivity as it changes.
STRINGS Searching for phrases in giant text (think Google or DNA).
SUCCINCT Most “linear size” data structures you know are much larger than they need to be, often by an order of magnitude. Some data structures require almost no space beyond the raw data but are still fast (think heaps, but much cooler).

(via Tim Freeman)

a Presentation from Simon Willnauer on optimization work performed on Lucene in 2011. The most interesting stuff here is the work done to replace an O(n^2) FuzzyQuery fuzzy-match algorithm with a FSM trie is extremely cool -- benchmarked at 214 times faster!

Using new Intel Core i7 instructions to speed up string manipulation. Fascinating stuff. SSE ftw

'Searching for all occurrences of a pattern in a text is a fundamental problem in computer science with applications in many other
fields, like NLP, information retrieval and computational biology. In the last two decades a general trend has appeared
trying to exploit the power of the word RAM model to speed-up the
performances of classical string matching algorithms. [...]
In this paper we use specialized word-size packed string matching instructions, based on the Intel streaming SIMD extensions (SSE) technology, to design very fast string matching algorithms in the case of short patterns.' Reminds me of http://en.wikipedia.org/wiki/Rabin%E2%80%93Karp_algorithm , but taking advantage of SIMD extensions, which should make things nice and speedy, at the cost of tying it to specific hardware platforms. (via Tony Finch)

Great comments -- the Burrows-Wheeler Transform is crazy stuff