1,534 research outputs found
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
A Survey on Array Storage, Query Languages, and Systems
Since scientific investigation is one of the most important providers of
massive amounts of ordered data, there is a renewed interest in array data
processing in the context of Big Data. To the best of our knowledge, a unified
resource that summarizes and analyzes array processing research over its long
existence is currently missing. In this survey, we provide a guide for past,
present, and future research in array processing. The survey is organized along
three main topics. Array storage discusses all the aspects related to array
partitioning into chunks. The identification of a reduced set of array
operators to form the foundation for an array query language is analyzed across
multiple such proposals. Lastly, we survey real systems for array processing.
The result is a thorough survey on array data storage and processing that
should be consulted by anyone interested in this research topic, independent of
experience level. The survey is not complete though. We greatly appreciate
pointers towards any work we might have forgotten to mention.Comment: 44 page
Tree model guided candidate generation for mining frequent subtrees from XML
Due to the inherent flexibilities in both structure and semantics, XML association rules mining faces few challenges, such as: a more complicated hierarchical data structure and ordered data context. Mining frequent patterns from XML documents can be recast as mining frequent tree structures from a database of XML documents. In this study, we model a database of XML documents as a database of rooted labeled ordered subtrees. In particular, we are mainly coneerned with mining frequent induced and embedded ordered subtrees. Our main contributions arc as follows. We describe our unique embedding list representation of the tree structure, which enables efficient implementation ofour Tree Model Guided (TMG) candidate generation. TMG is an optimal, non-redundant enumeration strategy which enumerates all the valid candidates that conform to the structural aspects of the data. We show through a mathematical model and experiments that TMG has better complexity compared to the commonly used join approach. In this paper, we propose two algorithms, MB3Miner and iMB3-Miner. MB3-Miner mines embedded subtrees. iMB3-Miner mines induced and/or embedded subtrees by using the maximum level of embedding constraint. Our experiments with both synthetic and real datasets against two well known algorithms for mining induced and embedded subtrees, demonstrate the effeetiveness and the efficiency of the proposed techniques
Kaskade: Graph Views for Efficient Graph Analytics
Graphs are an increasingly popular way to model real-world entities and
relationships between them, ranging from social networks to data lineage graphs
and biological datasets. Queries over these large graphs often involve
expensive subgraph traversals and complex analytical computations. These
real-world graphs are often substantially more structured than a generic
vertex-and-edge model would suggest, but this insight has remained mostly
unexplored by existing graph engines for graph query optimization purposes.
Therefore, in this work, we focus on leveraging structural properties of graphs
and queries to automatically derive materialized graph views that can
dramatically speed up query evaluation. We present KASKADE, the first graph
query optimization framework to exploit materialized graph views for query
optimization purposes. KASKADE employs a novel constraint-based view
enumeration technique that mines constraints from query workloads and graph
schemas, and injects them during view enumeration to significantly reduce the
search space of views to be considered. Moreover, it introduces a graph view
size estimator to pick the most beneficial views to materialize given a query
set and to select the best query evaluation plan given a set of materialized
views. We evaluate its performance over real-world graphs, including the
provenance graph that we maintain at Microsoft to enable auditing, service
analytics, and advanced system optimizations. Our results show that KASKADE
substantially reduces the effective graph size and yields significant
performance speedups (up to 50X), in some cases making otherwise intractable
queries possible
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