128,849 research outputs found
Indexer++: Workload-Aware Online Index Tuning With Transformers and Reinforcement Learning
With the increasing workload complexity in modern databases, the manual process of index selection is a challenging task. There is a growing need for a database with an ability to learn and adapt to evolving workloads. This paper proposes Indexer++, an autonomous, workload-aware, online index tuner. Unlike existing approaches, Indexer++ imposes low overhead on the DBMS, is responsive to changes in query workloads and swiftly selects indexes. Our approach uses a combination of text analytic techniques and reinforcement learning. Indexer++ consist of two phases: Phase (i) learns workload trends using a novel trend detection technique based on a pre-trained transformer model. Phase (ii) performs online, i.e., continuous or while the DBMS is processing workloads, index selection using a novel online deep reinforcement learning technique using our proposed priority experience sweeping. This paper provides an experimental evaluation of Indexer++ in multiple scenarios using benchmark (TPC-H) and real-world datasets (IMDB). In our experiments, Indexer++ effectively identifies changes in workload trends and selects the set of optimal indexes
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Benchmark-adjusted performance of US equity mutual funds and the issue of prospectus benchmarks
This study examines the impact of mismatch between prospectus benchmark and fund objectives on benchmark-adjusted fund performance and ranking in a sample of 1281 US equity mutual funds. All funds in our sample report S&P500 index as a prospectus benchmark, yet 2/3 of those are placed in the Morningstar category with risk and objectives different to those of the S&P500 index. We identify more appropriate ‘category benchmarks’ for those mismatched funds and obtain their benchmark-adjusted alphas using recent Angelidis et al. (J Bank Finance 37(5):1759–1776, 2013) methodology. We find that S&P500-adjusted alphas are higher than ‘category benchmark’-adjusted alphas in 61.2% of the cases. In terms of fund quartile rankings, 30% of winner funds lose that status when the prospectus benchmark is substituted with the one better matching their objectives. In the remaining performance quartiles, there is no clear advantage of using S&P 500 as a benchmark. Hence, the prospectus benchmark can mislead investors about fund’s relative performance and ranking, so any reference to performance in a fund’s prospectus should be treated with caution
Formal Representation of the SS-DB Benchmark and Experimental Evaluation in EXTASCID
Evaluating the performance of scientific data processing systems is a
difficult task considering the plethora of application-specific solutions
available in this landscape and the lack of a generally-accepted benchmark. The
dual structure of scientific data coupled with the complex nature of processing
complicate the evaluation procedure further. SS-DB is the first attempt to
define a general benchmark for complex scientific processing over raw and
derived data. It fails to draw sufficient attention though because of the
ambiguous plain language specification and the extraordinary SciDB results. In
this paper, we remedy the shortcomings of the original SS-DB specification by
providing a formal representation in terms of ArrayQL algebra operators and
ArrayQL/SciQL constructs. These are the first formal representations of the
SS-DB benchmark. Starting from the formal representation, we give a reference
implementation and present benchmark results in EXTASCID, a novel system for
scientific data processing. EXTASCID is complete in providing native support
both for array and relational data and extensible in executing any user code
inside the system by the means of a configurable metaoperator. These features
result in an order of magnitude improvement over SciDB at data loading,
extracting derived data, and operations over derived data.Comment: 32 pages, 3 figure
Defensive online portfolio selection
The class of defensive online portfolio selection algorithms,designed for fi nite investment horizon, is introduced. The Game Constantly Rebalanced Portfolio and the Worst Case Game Constantly Rebalanced Portfolio, are presented and theoretically analyzed. The analysis exploits the rich set of mathematical tools available by means of the connection between Universal Portfolios and the Game- Theoretic framework. The empirical performance of the Worst Case Game Constantly Rebalanced Portfolio algorithm is analyzed through numerical experiments concerning the FTSE 100, Nikkei 225, Nasdaq 100 and S&P500 stock markets for the time interval, from January 2007 to December 2009, which includes the credit crunch crisis from September 2008 to March 2009. The results emphasize the relevance of the proposed online investment algorithm which signi fi cantly outperformed the market index and the minimum variance Sharpe-Markowitz’s portfolio.on-line portfolio selection; universal portfolio; defensive strategy
Towards Smart Hybrid Fuzzing for Smart Contracts
Smart contracts are Turing-complete programs that are executed across a
blockchain network. Unlike traditional programs, once deployed they cannot be
modified. As smart contracts become more popular and carry more value, they
become more of an interesting target for attackers. In recent years, smart
contracts suffered major exploits, costing millions of dollars, due to
programming errors. As a result, a variety of tools for detecting bugs has been
proposed. However, majority of these tools often yield many false positives due
to over-approximation or poor code coverage due to complex path constraints.
Fuzzing or fuzz testing is a popular and effective software testing technique.
However, traditional fuzzers tend to be more effective towards finding shallow
bugs and less effective in finding bugs that lie deeper in the execution. In
this work, we present CONFUZZIUS, a hybrid fuzzer that combines evolutionary
fuzzing with constraint solving in order to execute more code and find more
bugs in smart contracts. Evolutionary fuzzing is used to exercise shallow parts
of a smart contract, while constraint solving is used to generate inputs which
satisfy complex conditions that prevent the evolutionary fuzzing from exploring
deeper paths. Moreover, we use data dependency analysis to efficiently generate
sequences of transactions, that create specific contract states in which bugs
may be hidden. We evaluate the effectiveness of our fuzzing strategy, by
comparing CONFUZZIUS with state-of-the-art symbolic execution tools and
fuzzers. Our evaluation shows that our hybrid fuzzing approach produces
significantly better results than state-of-the-art symbolic execution tools and
fuzzers
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