We formulate query-subquery nets and use them to create the first framework
for developing algorithms for evaluating queries to Horn knowledge bases with
the properties that: the approach is goal-directed; each subquery is processed
only once and each supplement tuple, if desired, is transferred only once;
operations are done set-at-a-time; and any control strategy can be used. Our
intention is to increase efficiency of query processing by eliminating
redundant computation, increasing flexibility and reducing the number of
accesses to the secondary storage.

With the increasing prevalence of location-aware devices, trajectory data has
been generated and collected in various application domains. Trajectory data
carries rich information that is useful for many data analysis tasks. Yet,
improper publishing and use of trajectory data could jeopardize individual
privacy. However, it has been shown that existing privacy-preserving trajectory
data publishing methods derived from partition-based privacy models, for
example k-anonymity, are unable to provide sufficient privacy protection.

As an essential operation in data cleaning, the similarity join has attracted
considerable attention from the database community. In this paper, we study
string similarity joins with edit-distance constraints, which find similar
string pairs from two large sets of strings whose edit distance is within a
given threshold. Existing algorithms are efficient either for short strings or
for long strings, and there is no algorithm that can efficiently and adaptively
support both short strings and long strings. To address this problem, we
propose a partition-based method called Pass-Join.

Querying uncertain data sets (represented as probability distributions)
presents many challenges due to the large amount of data involved and the
difficulties comparing uncertainty between distributions. The Earth Mover's
Distance (EMD) has increasingly been employed to compare uncertain data due to
its ability to effectively capture the differences between two distributions.
Computing the EMD entails finding a solution to the transportation problem,
which is computationally intensive.

One of the main challenges that the Semantic Web faces is the integration of
a growing number of independently designed ontologies. In this work, we present
PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not
only instances, but also relations and classes. Alignments at the instance
level cross-fertilize with alignments at the schema level. Thereby, our system
provides a truly holistic solution to the problem of ontology alignment. The
heart of the approach is probabilistic, i.e., we measure degrees of matchings
based on probability estimates.

Newly-released web applications often succumb to a "Success Disaster," where
overloaded database machines and resulting high response times destroy a
previously good user experience. Unfortunately, the data independence provided
by a traditional relational database system, while useful for agile
development, only exacerbates the problem by hiding potentially expensive
queries under simple declarative expressions.

With the rapid growth of internet technologies, Web has become a huge
repository of information and keeps growing exponentially under no editorial
control. However the human capability to read, access and understand Web
content remains constant. This motivated researchers to provide Web
personalized online services such as Web recommendations to alleviate the
information overload problem and provide tailored Web experiences to the Web
users. Recent studies show that Web usage mining has emerged as a popular
approach in providing Web personalization.

In many modern applications, data are received as infinite, rapid,
unpredictable and time- variant data elements that are known as data streams.
Systems which are able to process data streams with such properties are called
Data Stream Management Systems (DSMS). Due to the unpredictable and time-
variant properties of data streams as well as system, adaptivity of the DSMS is
a major requirement for each DSMS.

This paper presents a new solution, which adds a new database server on an
existing Oracle Multimaster Data replication system with Online Instantiation
method. During this time the system is down, because we cannot execute DML
statements on replication objects but we can only make queries. The time for
adding the new database server depends on the number of objects, on the
replication group and on the network conditions. We propose to add a new layer
between replication objects and the database sessions, which contain DML
statements.

The like regular expression predicate has been part of the SQL standard since
at least 1989. However, despite its popularity and wide usage, database vendors
provide only limited indexing support for regular expression queries which
almost always require a full table scan.

We study the problem of index deployment \textit{ordering}. Many database
applications deploy hundreds or thousands of indexes on large tables to speed
up query execution. An effective index deployment ordering can produce (1) a
prompt query runtime improvement and (2) a reduced total deployment time. Both
of these are essential qualities of design tools for quickly evolving
databases, but optimizing the problem is challenging because of complex index
interactions and a factorial number of possible solutions.

The relational model is the most commonly used data model for storing large
datasets, perhaps due to the simplicity of the tabular format which had
revolutionized database management systems. However, many real world objects
are recursive and associative in nature which makes storage in the relational
model difficult. The hypergraph model is a generalization of a graph model,
where each hypernode can be made up of other nodes or graphs and each hyperedge
can be made up of one or more edges. It may address the recursive and
associative limitations of relational model.

New hardware platforms, e.g. cloud, multi-core, etc., have led to a
reconsideration of database system architecture. Our Deuteronomy project
separates transactional functionality from data management functionality,
enabling a flexible response to exploiting new platforms. This separation
requires, however, that recovery is described logically. In this paper, we
extend current recovery methods to work in this logical setting. While this is
straightforward in principle, performance is an issue.

A high-quality, comprehensive product catalog is essential to the success of
Product Search engines and shopping sites such as Yahoo! Shopping, Google
Product Search or Bing Shopping. But keeping catalogs up-to-date becomes a
challenging task, calling for the need of automated techniques. In this paper,
we introduce the problem of product synthesis, a key component of catalog
creation and maintenance. Given a set of offers advertised by merchants, the
goal is to identify new products and add them to the catalog together with
their (structured) attributes.

Privacy Preserving Data Mining (PPDM) addresses the problem of developing
accurate models about aggregated data without access to precise information in
individual data record. A widely studied \emph{perturbation-based PPDM}
approach introduces random perturbation to individual values to preserve
privacy before data is published. Previous solutions of this approach are
limited in their tacit assumption of single-level trust on data miners.

A new compression method called difference-Huffman coding (DHC) is introduced
in this paper. It is verified empirically that DHC results in a smaller
multidimensional physical representation than those for other previously
published techniques (single count header compression, logical position
compression, base-offset compression and difference sequence compression). The
article examines how caching influences the expected retrieval time of the
multidimensional and table representations of relations. A model is proposed
for this, which is then verified with empirical data.

There have been several recent advancements in Machine Learning community on
the Entity Matching (EM) problem. However, their lack of scalability has
prevented them from being applied in practical settings on large real-life
datasets. Towards this end, we propose a principled framework to scale any
generic EM algorithm. Our technique consists of running multiple instances of
the EM algorithm on small neighborhoods of the data and passing messages across
neighborhoods to construct a global solution.

Spinnaker is an experimental datastore that is designed to run on a large
cluster of commodity servers in a single datacenter. It features key-based
range partitioning, 3-way replication, and a transactional get-put API with the
option to choose either strong or timeline consistency on reads. This paper
describes Spinnaker's Paxos-based replication protocol. The use of Paxos
ensures that a data partition in Spinnaker will be available for reads and
writes as long a majority of its replicas are alive.

Scaling up the sparse matrix-vector multiplication kernel on modern Graphics
Processing Units (GPU) has been at the heart of numerous studies in both
academia and industry. In this article we present a novel non-parametric,
self-tunable, approach to data representation for computing this kernel,
particularly targeting sparse matrices representing power-law graphs.

In this paper a tool called RDBNorma is proposed, that uses a novel approach
to represent a relational database schema and its functional dependencies in
computer memory using only one linked list and used for semi-automating the
process of relational database schema normalization up to third normal form.
This paper addresses all the issues of representing a relational schema along
with its functional dependencies using one linked list along with the
algorithms to convert a relation into second and third normal form by using
above representation.

We develop a novel method, based on the statistical concept of VC-dimension,
for selecting a small representative sample from a large database. The
execution of a query on the sample provides an accurate estimate for the
selectivity (or cardinality of the output) of each operation in the execution
of the query on the original large database. The size of the sample does not
depend on the size (number of tuples) of the database, but is a function of the
complexity of the queries we plan to run, measured by their VC-dimension.

There are many clustering methods, such as hierarchical clustering method.
Most of the approaches to the clustering of variables encountered in the
literature are of hierarchical type. The great majority of hierarchical
approaches to the clustering of variables are of agglomerative nature. The
agglomerative hierarchical approach to clustering starts with each observation
as its own cluster and then continually groups the observations into
increasingly larger groups. Higher Learning Institution (HLI) provides training
to introduce final-year students to the real working environment.

The Zebrafish Model Organism Database (ZFIN) provides a Web resource of
zebrafish genomic, genetic, developmental, and phenotypic data. Four different
ontologies are currently used to annotate data to the most specific term
available facilitating a better comparison between inter-species data. In
addition, ontologies are used to help users find and cluster data more quickly
without the need of knowing the exact technical name for a term.

Our Chemical e-Science Information Cloud (ChemCloud) - a Semantic Web based
eScience infrastructure - integrates and automates a multitude of databases,
tools and services in the domain of chemistry, pharmacy and bio-chemistry
available at the Fachinformationszentrum Chemie (FIZ Chemie), at the Freie
Universitaet Berlin (FUB), and on the public Web.

A key goal of bioinformatics is to create database systems and software
platforms capable of storing and analysing large sets of biological data.
Hundreds of biological databases are now available and provide access to huge
amount of biological data. SGD, Yeastract, CYGD-MIPS, BioGrid and PhosphoGrid
are five of the most visited databases by the yeast community. These sources
provide complementary data on biological entities. Biologists are brought
systematically to query these data sources in order to analyse the results of
their experiments.

A boolean expression is in read-once form if each of its variables appears
exactly once. When the variables denote independent events in a probability
space, the probability of the event denoted by the whole expression in
read-once form can be computed in polynomial time (whereas the general problem
for arbitrary expressions is #P-complete). Known approaches to checking
read-once property seem to require putting these expressions in disjunctive
normal form.

Ontologies such as taxonomies, product catalogs or web directories are
heavily used and hence evolve frequently to meet new requirements or to better
reflect the current instance data of a domain. To effectively manage the
evolution of ontologies it is essential to identify the difference (Diff)
between two ontology versions. We propose a novel approach to determine an
expressive and invertible diff evolution mapping between given versions of an
ontology.

An answer to a query has a well-defined lineage expression (alternatively
called how-provenance) that explains how the answer was derived. Recent work
has also shown how to compute the lineage of a non-answer to a query. However,
the cause of an answer or non-answer is a more subtle notion and consists, in
general, of only a fragment of the lineage. In this paper, we adapt Halpern,
Pearl, and Chockler's recent definitions of causality and responsibility to
define the causes of answers and non-answers to queries, and their degree of
responsibility.

In this paper a novel fragile watermarking scheme is proposed to detect,
localize and recover malicious modifications in relational databases. In the
proposed scheme, all tuples in the database are first securely divided into
groups. Then watermarks are embedded and verified group-by-group independently.
By using the embedded watermark, we are able to detect and localize the
modification made to the database and even we recover the true data from the
database modified locations. Our experimental results show that this scheme is
so qualified; i.e.

Problem statement: Clustering has a number of techniques that have been
developed in statistics, pattern recognition, data mining, and other fields.
Subspace clustering enumerates clusters of objects in all subspaces of a
dataset. It tends to produce many over lapping clusters. Approach: Subspace
clustering and projected clustering are research areas for clustering in high
dimensional spaces. In this research we experiment three clustering oriented
algorithms, PROCLUS, P3C and STATPC.

Matching dependencies (MDs) were introduced to specify the identification or
matching of certain attribute values in pairs of database tuples when some
similarity conditions are satisfied. Their enforcement can be seen as a natural
generalization of entity resolution. In what we call the "pure case" of MDs,
any value from the underlying data domain can be used for the value in common
that does the matching. We investigate the semantics and properties of data
cleaning through the enforcement of matching dependencies for the pure case.

Matching dependencies were recently introduced as declarative rules for data
cleaning and entity resolution. Enforcing a matching dependency on a database
instance identifies the values of some attributes for two tuples, provided that
the values of some other attributes are sufficiently similar. Assuming the
existence of matching functions for making two attributes values equal, we
formally introduce the process of cleaning an instance using matching
dependencies, as a chase-like procedure.

This research is about an online forum designed and developed to improve the
communication process between alumni, new, old and upcoming students. In this
research paper we present targeted problems, designed architecture, used
technologies in development and final end product in detail.

Top-$k$ queries allow end-users to focus on the most important (top-$k$)
answers amongst those which satisfy the query. In traditional databases, a user
defined score function assigns a score value to each tuple and a top-$k$ query
returns $k$ tuples with the highest score. In uncertain database, top-$k$
answer depends not only on the scores but also on the membership probabilities
of tuples. Several top-$k$ definitions covering different aspects of
score-probability interplay have been proposed in recent
past~\cite{R10,R4,R2,R8}.

Date and Darwen have proposed a theory of types, the latter forms the basis
of a detailed presentation of a panoply of simple and complex types. However,
this proposal has not been structured in a formal system. Specifically, Date
and Darwen haven't indicated the formalism of the type system that corresponds
to the type theory established.

This project addressed the conceptual fundamentals of data storage,
investigating techniques for provision of highly generic storage facilities
that can be tailored to produce various individually customised storage
infrastructures, compliant to the needs of particular applications. This
requires the separation of mechanism and policy wherever possible.

Similarity search methods are widely used as kernels in various machine
learning applications. Nearest neighbor search (NNS) algorithms are often used
to retrieve similar entries, given a query. While there exist efficient
techniques for exact query lookup using hashing, similarity search using exact
nearest neighbors is known to be a hard problem and in high dimensions, best
known solutions offer little improvement over a linear scan.

Searching learning or rules in relational database for data mining purposes
with characteristic or classification/discriminant rule in attribute oriented
induction technique can be quicker, easy, and simple with simple SQL statement.
With just only one simple SQL statement, characteristic and classification rule
can be created simultaneously.

Multidimensional in data warehouse is a compulsion and become the most
important for information delivery, without multidimensional Multidimensional
in data warehouse is a compulsion and become the most important for information
delivery, without multidimensional datawarehouse is incomplete.
Multidimensional give ability to analyze business measurement in many different
ways. Multidimensional is also synonymous with online analytical processing
(OLAP). By using some concepts in datawarehouse like slice-dice,drill down and
roll up will increase the ability of multidimensional datawarehouse.

The amounts of data available to decision makers are increasingly important,
given the network availability, low cost storage and diversity of applications.
To maximize the potential of these data within the National Social Security
Fund (NSSF) in Tunisia, we have built a data warehouse as a multidimensional
database, cleaned, homogenized, historicized and consolidated. We used Oracle
Warehouse Builder to extract, transform and load the source data into the Data
Warehouse, by applying the KDD process. We have implemented the Data Warehouse
as an Oracle OLAP.

Regular tree grammars and regular path expressions constitute core constructs
widely used in programming languages and type systems. Nevertheless, there has
been little research so far on reasoning frameworks for path expressions where
node cardinality constraints occur along a path in a tree. We present a logic
capable of expressing deep counting along paths which may include arbitrary
recursive forward and backward navigation.

We study the problem of providing workflow data provenance without revealing
the functionality of any module. We develop a model that formalizes the notion
of privacy of modules embedded in a workflow structure as a natural extension
of privacy of standalone modules. Our model shows that by hiding a small amount
of carefully chosen data, one can ensure privacy of all modules over an
unbounded number of executions. The problem of identifying the smallest
possible amount of such data is NP-hard, and in the full generality of our
model it is in fact even hard to get a good approximation.

Functional dependencies - traditional, approximate and conditional are of
critical importance in relational databases, as they inform us about the
relationships between attributes. They are useful in schema normalization, data
rectification and source selection. However, probabilistic databases neither
have these dependencies defined for them, nor are fast algorithms available to
evaluate their confidences. In this paper, we define the logical extensions of
various forms of functional dependencies for probabilistic databases. We
explore the connections between these dependencies.

This paper deals with personalization of annotated OLAP systems. Data
constellation is extended to support annotations and user preferences.
Annotations reflect the decision-maker experience whereas user preferences
enable users to focus on the most interesting data. User preferences allow
annotated contextual recommendations helping the decision-maker during his/her
multidimensional navigations.

Inferring an appropriate DTD or XML Schema Definition (XSD) for a given
collection of XML documents essentially reduces to learning deterministic
regular expressions from sets of positive example words. Unfortunately, there
is no algorithm capable of learning the complete class of deterministic regular
expressions from positive examples only, as we will show. The regular
expressions occurring in practical DTDs and XSDs, however, are such that every
alphabet symbol occurs only a small number of times.

Association rule mining is an active data mining research area and most ARM
algorithms cater to a centralized environment. Centralized data mining to
discover useful patterns in distributed databases isn't always feasible because
merging data sets from different sites incurs huge network communication costs.
In this paper, an Improved algorithm based on good performance level for data
mining is being proposed.

To obtain good system performance, a DBA must choose a set of indices that is
appropriate for the workload. The system can aid in this challenging task by
providing recommendations for the index configuration. We propose a new index
recommendation technique, termed semi-automatic tuning, that keeps the DBA "in
the loop" by generating recommendations that use feedback about the DBA's
preferences. The technique also works online, which avoids the limitations of
commercial tools that require the workload to be known in advance.

Alloy is a lightweight modeling formalism based on relational algebra. In
prior work with Fisler, Giannakopoulos, Krishnamurthi, and Yoo, we have
presented a tool, Alchemy, that compiles Alloy specifications into
implementations that execute against persistent databases. The foundation of
Alchemy is an algorithm for rewriting relational algebra formulas into code for
database transactions. In this paper we report on recent progress in improving
the robustness and efficiency of this transformation.

Numerous generalization techniques have been proposed for privacy preserving
data publishing. Most existing techniques, however, implicitly assume that the
adversary knows little about the anonymization algorithm adopted by the data
publisher. Consequently, they cannot guard against privacy attacks that exploit
various characteristics of the anonymization mechanism. This paper provides a
practical solution to the above problem. First, we propose an analytical model
for evaluating disclosure risks, when an adversary knows everything in the
anonymization process, except the sensitive values.

In this paper, we try to focus the reader's interest on the problems that
transactional systems have to resolve for taking advantage of commutativity in
a serializable and recoverable way. Our framework is, (as others), based on the
use of conditional commutativity on abstract date types. We present new
features that have not been found in the literature hitherto, that both
increase concurrency and simplify recovery.

Several propositions were done to provide adapted concurrency control to
object-oriented databases. However, most of these proposals miss the fact that
considering solely read and write access modes on instances may lead to less
parallelism than in relational databases!

Previous work reports about SXSI, a fast XPath engine which executes tree
automata over compressed XML indexes. Here, reasons are investigated why SXSI
is so fast. It is shown that tree automata can be used as a general framework
for fine grained XML query optimization. We define the "relevant nodes" of a
query as those nodes that a minimal automaton must touch in order to answer the
query. This notion allows to skip many subtrees during execution, and, with the
help of particular tree indexes, even allows to skip internal nodes of the
tree.

Finding multilevel association rules in transaction databases is most
commonly seen in is widely used in data mining. In this paper, we present a
model of mining multilevel association rules which satisfies the different
minimum support at each level, we have employed fuzzy set concepts, multi-level
taxonomy and different minimum supports to find fuzzy multilevel association
rules in a given transaction data set. Apriori property is used in model to
prune the item sets. The proposed model adopts a topdown progressively
deepening approach to derive large itemsets.

Since Chen's Entity-Relationship (ER) model, conceptual modeling has been
playing a fundamental role in relational data design. In this paper we consider
an extended ER (EER) model enriched with cardinality constraints, disjointness
assertions, and is-a relations among both entities and relationships. In this
setting, we consider the case of incomplete data, which is likely to occur, for
instance, when data from different sources are integrated. In such a context,
we address the problem of providing correct answers to conjunctive queries by
reasoning on the schema.

This paper proposes a data tree-rewriting framework for modeling evolving
documents. The framework is close to Guarded Active XML, a platform used for
handling XML repositories evolving through web services. We focus on automatic
verification of properties of evolving documents that can contain data from an
infinite domain. We establish the boundaries of decidability, and show that
verification of a {\em positive} fragment that can handle recursive service
calls is decidable.

Time is one of the most difficult aspects to handle in real world
applications such as database systems. Relational database management systems
proposed by Codd offer very little built-in query language support for temporal
data management. The model itself incorporates neither the concept of time nor
any theory of temporal semantics. Many temporal extensions of the relational
model have been proposed and some of them are also implemented. This paper
offers a brief introduction to temporal database research.

Data mining is the task of discovering interesting patterns from large
amounts of data. There are many data mining tasks, such as classification,
clustering, association rule mining, and sequential pattern mining. Sequential
pattern mining finds sets of data items that occur together frequently in some
sequences.

There is a considerable body of work on sequence mining of Web Log Data. We
are using One Pass frequent Episode discovery (or FED) algorithm, takes a
different approach than the traditional apriori class of pattern detection
algorithms. In this approach significant intervals for each Website are
computed first (independently) and these interval used for detecting frequent
patterns/Episode and then the Analysis is performed on Significant Intervals
and frequent patterns That can be used to forecast the user's behavior using
previous trends and this can be also used for advertising purpose.

As mobile devices with positioning capabilities continue to proliferate, data
management for so-called trajectory databases that capture the historical
movements of populations of moving objects becomes important. This paper
considers the querying of such databases for convoys, a convoy being a group of
objects that have traveled together for some time. More specifically, this
paper formalizes the concept of a convoy query using density-based notions, in
order to capture groups of arbitrary extents and shapes.

Flash memory is widely used as the secondary storage in lightweight computing
devices due to its outstanding advantages over magnetic disks. Flash memory has
many access characteristics different from those of magnetic disks, and how to
take advantage of them is becoming an important research issue. There are two
existing approaches to storing data into flash memory: page-based and
log-based. The former has good performance for read operations, but poor
performance for write operations.

The problem of developing models and algorithms for multilevel association
mining pose for new challenges for mathematics and computer science. These
problems become more challenging, when some form of uncertainty like fuzziness
is present in data or relationships in data. This paper proposes a multilevel
fuzzy association rule mining models for extracting knowledge implicit in
transactions database with different support at each level. The proposed
algorithm adopts a top-down progressively deepening approach to derive large
itemsets.

With the growing focus on semantic searches and interpretations, an
increasing number of standardized vocabularies and ontologies are being
designed and used to describe data. We investigate the querying of objects
described by a tree-structured ontology. Specifically, we consider the case of
finding the top-k best pairs of objects that have been annotated with terms
from such an ontology when the object descriptions are available only at
runtime. We consider three distance measures.

Certainly, nowadays knowledge discovery or extracting knowledge from large
amount of data is a desirable task in competitive businesses. Data mining is a
main step in knowledge discovery process. Meanwhile frequent patterns play
central role in data mining tasks such as clustering, classification, and
association analysis. Identifying all frequent patterns is the most time
consuming process due to a massive number of candidate patterns. For the past
decade there have been an increasing number of efficient algorithms to mine the
frequent patterns.

Real-time databases deal with time-constrained data and time-constrained
transactions. The design of this kind of databases requires the introduction of
new concepts to support both data structures and the dynamic behaviour of the
database. In this paper, we give an overview about different aspects of
real-time databases and we clarify requirements of their modelling. Then, we
present a framework for real-time database design and describe its fundamental
operations.

Search engine companies collect the "database of intentions", the histories
of their users' search queries. These search logs are a gold mine for
researchers. Search engine companies, however, are wary of publishing search
logs in order not to disclose sensitive information.

Publishing person-specific transactions in an anonymous form is increasingly
required by organizations. Recent approaches ensure that potentially
identifying information (e.g., a set of diagnosis codes) cannot be used to link
published transactions to persons' identities, but all are limited in
application because they incorporate coarse privacy requirements (e.g.,
protecting a certain set of m diagnosis codes requires protecting all m-sized
sets), do not integrate utility requirements, and tend to explore a small
portion of the solution space.

Computer-based information technologies have been extensively used to help
many organizations, private companies, and academic and education institutions
manage their processes and information systems hereby become their nervous
centre. The explosion of massive data sets created by businesses, science and
governments necessitates intelligent and more powerful computing paradigms so
that users can benefit from this data. Therefore most new-generation database
applications demand intelligent information management to enhance efficient
interactions between database and the users.

As XML becomes ubiquitous and XML storage and processing becomes more
efficient, the range of use cases for these technologies widens daily. One
promising area is the integration of XML and data warehouses, where an
XML-native database stores multidimensional data and processes OLAP queries
written in the XQuery interrogation language. This paper explores issues
arising in the implementation of such a data warehouse. We first compare
approaches for multidimensional data modelling in XML, then describe how
typical OLAP queries on these models can be expressed in XQuery.

This paper presents a novel approach for the integration of a set of XML
Schemas. The proposed approach is specialized for XML, is almost automatic,
semantic and "light". As a further, original, peculiarity, it is parametric
w.r.t. a "severity" level against which the integration task is performed. The
paper describes the approach in all details, illustrates various theoretical
results, presents the experiments we have performed for testing it and,
finally, compares it with various related approaches already proposed in the
literature.

In the last years, a lot of attention has been paid to the development of
solid foundations for the composition and inversion of schema mappings. In this
paper, we review the proposals for the semantics of these crucial operators.
For each of these proposals, we concentrate on the three following problems:
the definition of the semantics of the operator, the language needed to express
the operator, and the algorithmic issues associated to the problem of computing
the operator.

Privacy preserving data publishing has attracted considerable research
interest in recent years. Among the existing solutions, {\em
$\epsilon$-differential privacy} provides one of the strongest privacy
guarantees. Existing data publishing methods that achieve
$\epsilon$-differential privacy, however, offer little data utility. In
particular, if the output dataset is used to answer count queries, the noise in
the query answers can be proportional to the number of tuples in the data,
which renders the results useless.

We study the termination problem of the chase algorithm, a central tool in
various database problems such as the constraint implication problem,
Conjunctive Query optimization, rewriting queries using views, data exchange,
and data integration. The basic idea of the chase is, given a database instance
and a set of constraints as input, to fix constraint violations in the database
instance. It is well-known that, for an arbitrary set of constraints, the chase
does not necessarily terminate (in general, it is even undecidable if it does
or not).

High-volume, high-speed data streams may overwhelm the capabilities of stream
processing systems; techniques such as data prioritization, avoidance of
unnecessary processing and on-demand result production may be necessary to
reduce processing requirements. However, the dynamic nature of data streams, in
terms of both rate and content, makes the application of such techniques
challenging. Such techniques have been addressed in the context of static and
centralized query optimization; however, they have not been fully addressed for
data stream management systems.

Recently, many content sites have started encouraging their users to engage
in social activities such as adding buddies on Yahoo! Travel and sharing
articles with their friends on New York Times.

In recent years, column stores (or C-stores for short) have emerged as a
novel approach to deal with read-mostly data warehousing applications.
Experimental evidence suggests that, for certain types of queries, the new
features of C-stores result in orders of magnitude improvement over traditional
relational engines. At the same time, some C-store proponents argue that
C-stores are fundamentally different from traditional engines, and therefore
their benefits cannot be incorporated into a relational engine short of a
complete rewrite.

We present the design and development of a data stream system that captures
data uncertainty from data collection to query processing to final result
generation. Our system focuses on data that is naturally modeled as continuous
random variables. For such data, our system employs an approach grounded in
probability and statistical theory to capture data uncertainty and integrates
this approach into high-volume stream processing. The first component of our
system captures uncertainty of raw data streams from sensing devices.

Recent excitement in the database community surrounding new
applications?analytic, scientific, graph, geospatial, etc.?has led to an
explosion in research on database storage systems. New storage systems are
vital to the database community, as they are at the heart of making database
systems perform well in new application domains. Unfortunately, each such
system also represents a substantial engineering effort including a great deal
of duplication of mechanisms for features such as transactions and caching.

R is a numerical computing environment that is widely popular for statistical
data analysis. Like many such environments, R performs poorly for large
datasets whose sizes exceed that of physical memory. We present our vision of
RIOT (R with I/O Transparency), a system that makes R programs I/O-efficient in
a way transparent to the users. We describe our experience with RIOT-DB, an
initial prototype that uses a relational database system as a backend.

Collaborative web applications such as Facebook, Flickr and Yelp present new
challenges for storing and querying large amounts of data. As users and
developers are focused more on performance than single copy consistency or the
ability to perform ad-hoc queries, there exists an opportunity for a
highly-scalable system tailored specifically for relaxed consistency and
pre-computed queries.

Energy costs are quickly rising in large-scale data centers and are soon
projected to overtake the cost of hardware. As a result, data center operators
have recently started turning into using more energy-friendly hardware. Despite
the growing body of research in power management techniques, there has been
little work to date on energy efficiency from a data management software
perspective.

Natural language user interfaces to database systems have been studied for
several decades now. They have mainly focused on parsing and interpreting
natural language queries to generate them in a formal database language.

The traditional architecture for a DBMS engine has the recovery, concurrency
control and access method code tightly bound together in a storage engine for
records. We propose a different approach, where the storage engine is factored
into two layers (each of which might have multiple heterogeneous instances). A
Transactional Component (TC) works at a logical level only: it knows about
transactions and their "logical" concurrency control and undo/redo recovery,
but it does not know about page layout, B-trees etc. A Data Component (DC)
knows about the physical storage structure.

To date, the principal use case for schema matching research has been as a
precursor for code generation, i.e., constructing mappings between schema
elements with the end goal of data transfer. In this paper, we argue that
schema matching plays valuable roles independent of mapping construction,
especially as schemata grow to industrial scales.

Analytical processing on XML repositories is usually enabled by designing
complex data transformations that shred the documents into a common data
warehousing schema. This can be very time-consuming and costly, especially if
the underlying XML data has a lot of variety in structure, and only a subset of
attributes constitutes meaningful dimensions and facts. Today, there is no tool
to explore an XML data set, discover interesting attributes, dimensions and
facts, and rapidly prototype an OLAP solution.

Today's sequencing technology allows sequencing an individual genome within a
few weeks for a fraction of the costs of the original Human Genome project.
Genomics labs are faced with dozens of TB of data per week that have to be
automatically processed and made available to scientists for further analysis.
This paper explores the potential and the limitations of using relational
database systems as the data processing platform for high-throughput genomics.
In particular, we are interested in the storage management for high-throughput
sequence data and in leveraging SQL and user-defined func

We introduce a world vision in which data is endowed with memory. In this
data-centric systems paradigm, data items can be enabled to retain all or some
of their previous values. We call this ability "remembrance" and posit that it
empowers significant leaps in the security, availability, and general
operational dimensions of systems. With the explosion in cheap, fast memories
and storage, large-scale remembrance will soon become practical. Here, we
introduce and explore the advantages of such a paradigm and the challenges in
making it a reality.

Background knowledge is an important factor in privacy preserving data
publishing. Distribution-based background knowledge is one of the well studied
background knowledge. However, to the best of our knowledge, there is no
existing work considering the distribution-based background knowledge in the
worst case scenario, by which we mean that the adversary has accurate knowledge
about the distribution of sensitive values according to some tuple attributes.
Considering this worst case scenario is essential because we cannot overlook
any breaching possibility.

Large systems biology projects can encompass several workgroups often located
in different countries.