NDLI: Database researchers: plumbers or thinkers?

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Map-reduce extensions and recursive queries

Novel techniques to reduce search space in multiple minimum supports-based frequent pattern mining algorithms

GPX-matcher: a generic boolean predicate-based XPath expression matcher

Energy proportionality for disk storage using replication

Native support of multi-tenancy in RDBMS for software as a service

Answering tree pattern queries using views: a revisit

Keyword-based, context-aware selection of natural language query patterns

Efficient answering of set containment queries for skewed item distributions

SeMiTri: a framework for semantic annotation of heterogeneous trajectories

TAGs: scalable threshold-based algorithms for proximity computation in graphs

Fast random graph generation

Finding closed frequent item sets by intersecting transactions

Data integration with dependent sources

Predicting completion times of batch query workloads using interaction-aware models and simulation

Queries on dates: fast yet not blind

SocialSearch: enhancing entity search with social network matching

Schema matching and mapping: from usage to evaluation

A probabilistic XML merging tool

Database researchers: plumbers or thinkers?

Mining closed discriminative dyadic sequential patterns

An optimal strategy for monitoring top-k queries in streaming windows

Caching query-biased snippets for efficient retrieval

SLA-tree: a framework for efficiently supporting SLA-based decisions in cloud computing

Dynamic reasoning on XML updates

Unified structure and content search for personal information management systems

Subspace clustering for indexing high dimensional data: a main memory index based on local reductions and individual multi-representations

Efficient execution plans for distributed skyline query processing

Projection for XML update optimization

Symmetrizations for clustering directed graphs

Aspect-oriented relational algebra

Constructing concept relation network and its application to personalized web search

Memory-efficient frequent-itemset mining

Designing integration flows using hypercubes

Road crash proneness prediction using data mining

Big data and cloud computing: current state and future opportunities

Taking the OXPath down the deep web

Sequenced event set pattern matching

Fast and accurate computation of equi-depth histograms over data streams

Efficient and scalable data evolution with column oriented databases

On enhancing scalability for distributed RDF/S stores

Algebraic incremental maintenance of XML views

TopRecs: Top-k algorithms for item-based collaborative filtering

On (not) indexing quadratic form distance by metric access methods

Probabilistic range queries for uncertain trajectories on road networks

Efficient reverse skyline retrieval with arbitrary non-metric similarity measures

Efficient discovery of frequent subgraph patterns in uncertain graph databases

Synopses for probabilistic data over large domains

Effective and efficient sampling methods for deep web aggregation queries

Link-based hidden attribute discovery for objects on Web

Experience in Continuous analytics as a Service (CaaaS)

The hidden web, XML and the Semantic Web: scientific data management perspectives

SPRINT: ranking search results by paths

Making interval-based clustering rank-aware

Real-time approximate Range Motif discovery & data redundancy removal algorithm

A query optimization assistant for XPath

TPM: supporting pattern matching queries for road-network trajectory data

QueryViz: helping users understand SQL queries and their patterns

True language-level SQL debugging

SITAC: discovering semantically identical temporally altering concepts in text archives

Unraveling multi-dimensional data using pDView

RanKloud: a scalable ranked query processing framework on hadoop

Database researchers: plumbers or thinkers?

Content Provider	ACM Digital Library
Author	Weikum, Gerhard
Abstract	DB researchers have traditionally focused on engine-centered issues such as indexing, query processing, and transactions. Data mining has broadened the community's viewpoint towards algorithmic and statistical issues. However, DB research has always had a tendency to shy away from seemingly elusive long-term challenges with AI flavor. On the other hand, the current explosion of digital content in enterprises and the Internet, is mostly caused by user-created information like text, tags, photos, videos, and not by seeing more well-designed databases of the traditional kind. In this situation, I question the traditional skepticism of DB researchers towards "AI-complete" problems and the DB community's reluctance to embark on seemingly non-DB-ish grand challenges. Big questions that I see as great opportunities also for DB research include: 1) automatic extraction of relational facts from natural-language text and multimodal contexts [4, 6, 21], 2) automatic disambiguation of named-entity mentions and general phrases in text and speech [10, 11], 3) large-scale gathering of factual-knowledge candidates and their reconciliation into comprehensive knowledge bases [1, 2, 8, 13, 19], 4) reasoning on uncertain hypotheses, for knowledge discovery and semantic search [9, 14, 16, 17, 20], 5) deep and real-time question answering, e.g., to enable computers to win quiz game shows [7], 6) machine-reading of scientific publications and fictional literature, to enable corpus-wide analyses and enable researchers in science and humanities to develop hypotheses and quickly focus on the most relevant issues [3, 5]. I believe that successfully tackling these topics requires efficient data-centric algorithms, scalable methods and architectures, and system-level thinking - virtues that are richly available in the DB research community. Moreover, I would encourage our community to look across the fence and get more engaged on the exciting challenges outside the traditionally narrow boundaries of the DB realm. I will illustrate these points by examples from my own research on knowledge management [12, 15, 18, 19]. Breakthroughs will require long-term stamina. In the meantime, steady incremental progress is better than not embarking on these important problems at all.
Starting Page	9
Ending Page	10
Page Count	2
File Format	PDF
ISBN	9781450305280
DOI	10.1145/1951365.1951368
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-03-21
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Ai applications Disambiguation Scalability Machine reading Information extraction Knowledge management Robustness
Content Type	Text
Resource Type	Article

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