One-dimensional and multi-dimensional substring selectivity estimation

With the increasing importance of XML, LDAP directories, and text-based information sources on the Internet, there is an ever-greater need to evaluate queries involving (sub)string matching. In many cases, matches need to be on multiple attributes/dimensions, with correlations between the multiple dimensions. Effective query optimization in this context requires good selectivity estimates. In this paper, we use pruned count-suffix trees (PSTs) as the basic data structure for substring selectivity estimation. For the 1-D problem, we present a novel technique called MO (Maximal Overlap). We then develop and analyze two 1-D estimation algorithms, MOC and MOLC, based on MO and a constraint-based characterization of all possible completions of a given PST. For the k -D problem, we first generalize PSTs to multiple dimensions and develop a space- and time-efficient probabilistic algorithm to construct k -D PSTs directly. We then show how to extend MO to multiple dimensions. Finally, we demonstrate, both analytically and experimentally, that MO is both practical and substantially superior to competing algorithms.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42330/1/778-9-3-214_00090214.pd

Childhood intellectual disability and parents' mental health: integrating social, psychological and genetic influences.

BACKGROUND: Intellectual disability has a complex effect on the well-being of affected individuals and their families. Previous research has identified multiple risk and protective factors for parental mental health, including socioeconomic circumstances and child behaviour. AIMS: This study explored whether genetic cause of childhood intellectual disability contributes to parental well-being. METHOD: Children from across the UK with intellectual disability due to diverse genetic causes were recruited to the IMAGINE-ID study. Primary carers completed the Development and Well-being Assessment, including a measure of parental distress (Everyday Feeling Questionnaire). Genetic diagnoses were broadly categorised into aneuploidy, chromosomal rearrangements, copy number variants (CNVs) and single nucleotide variants. RESULTS: Compared with the UK general population, IMAGINE-ID parents (n = 888) reported significantly elevated emotional distress (Cohen's d = 0.546). Within-sample variation was related to recent life events and the perceived impact of children's difficulties. Impact was predicted by child age, physical disability, autistic characteristics and other behavioural difficulties. Genetic diagnosis also predicted impact, indirectly influencing parental well-being. Specifically, CNVs were associated with higher impact, not explained by CNV inheritance, neighbourhood deprivation or family structure. CONCLUSIONS: The mental health of parents caring for a child with intellectual disability is influenced by child and family factors, converging on parental appraisal of impact. We found that genetic aetiologies, broadly categorised, also influence impact and thereby family risks. Recognition of these risk factors could improve access to support for parents, reduce their long-term mental health needs and improve well-being of individuals with intellectual disability.This work was supported by the UK Medical Research Council (grant number G101400 to K.B.), UK Medical Research Council and Medical Research Foundation (grant number MR-N022572-1 to the IMAGINE-ID study; Principle Investigators: David H. Skuse, F Lucy Raymond, Jeremy Hall, Marianne Van den Bree, Michael J. Hall) and the Baily Thomas Charitable Trust (to K.B.)

Effects of substituting rare-earth ion R by non-magnetic impurities in R2BaNiO5R_2BaNiO_5 - theory and numerical DMRG results

In this paper we study the effect of substituting R (rare-earth ion) by non-magnetic ions in the spin-1 chain material $R_2BaNiO_5$. Using a strong-coupling expansion and numerical density matrix renormalization group calculations, we show that spin-wave bound states are formed at the impurity site. Experimental consequences of the bound states are pointed out.Comment: 5 pages, 4 postscript figure

LOF: Identifying density-based local outliers

For many KDD applications, such as detecting criminal activities in E-commerce, finding the rare instances or the outliers, can be more interesting than finding the common patterns. Existing work in outlier detection regards being an outlier as a binary property. In this paper, we contend that for many scenarios, it is more meaningful to assign to each object a degree of being an outlier. This degree is called the local outlier factor (LOF) of an object. It is local in that the degree depends on how isolated the object is with respect to the surrounding neighborhood. We give a detailed formal analysis showing that LOF enjoys many desirable properties. Using realworld datasets, we demonstrate that LOF can be used to find outliers which appear to be meaningful, but can otherwise not be identified with existing approaches. Finally, a careful performance evaluation of our algorithm confirms we show that our approach of finding local outliers can be practical