GENERIC FRAMEWORKS FOR INTERACTIVE PERSONALIZED INTERESTING PATTERN DISCOVERY

The traditional frequent pattern mining algorithms generate an exponentially large number of patterns of which a substantial portion are not much significant for many data analysis endeavours. Due to this, the discovery of a small number of interesting patterns from the exponentially large number of frequent patterns according to a particular user\u27s interest is an important task. Existing works on patter

Turbo-charging hidden database samplers with overflowing queries and skew reduction

Recently, there has been growing interest in random sampling from online hidden databases. These databases reside behind form-like web interfaces which allow users to execute search queries by spec-ifying the desired values for certain attributes, and the system re-sponds by returning a few (e.g., top-k) tuples that satisfy the se-lection conditions, sorted by a suitable scoring function. In this paper, we consider the problem of uniform random sampling over such hidden databases. A key challenge is to eliminate the skew of samples incurred by the selective return of highly ranked tuples. To address this challenge, all state-of-the-art samplers share a com-mon approach: they do not use overflowing queries. This is done in order to avoid favoring highly ranked tuples and thus incurring high skew in the retrieved samples. However, not considering over-flowing queries substantially impacts sampling efficiency. In this paper, we propose novel sampling techniques which do leverage overflowing queries. As a result, we are able to signif-icantly improve sampling efficiency over the state-of-the-art sam-plers, while at the same time substantially reduce the skew of gen-erated samples. We conduct extensive experiments over synthetic and real-world databases to illustrate the superiority of our tech-niques over the existing ones. 1

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Maritime expressions:a corpus based exploration of maritime metaphors

This study uses a purpose-built corpus to explore the linguistic legacy of Britain’s maritime history found in the form of hundreds of specialised ‘Maritime Expressions’ (MEs), such as TAKEN ABACK, ANCHOR and ALOOF, that permeate modern English. Selecting just those expressions commencing with ’A’, it analyses 61 MEs in detail and describes the processes by which these technical expressions, from a highly specialised occupational discourse community, have made their way into modern English. The Maritime Text Corpus (MTC) comprises 8.8 million words, encompassing a range of text types and registers, selected to provide a cross-section of ‘maritime’ writing. It is analysed using WordSmith analytical software (Scott, 2010), with the 100 million-word British National Corpus (BNC) as a reference corpus. Using the MTC, a list of keywords of specific salience within the maritime discourse has been compiled and, using frequency data, concordances and collocations, these MEs are described in detail and their use and form in the MTC and the BNC is compared. The study examines the transformation from ME to figurative use in the general discourse, in terms of form and metaphoricity. MEs are classified according to their metaphorical strength and their transference from maritime usage into new registers and domains such as those of business, politics, sports and reportage etc. A revised model of metaphoricity is developed and a new category of figurative expression, the ‘resonator’, is proposed. Additionally, developing the work of Lakov and Johnson, Kovesces and others on Conceptual Metaphor Theory (CMT), a number of Maritime Conceptual Metaphors are identified and their cultural significance is discussed