Distributed Databases

Domain Name System (DNS) is the system for the mapping between easily memorizable host names and their IP addresses. Due to its criticality, security extensions to DNS have been proposed in an Internet Engineering Task Force (IETF) working group to provide authentication. In this paper, we point out two difficulties in the current DNSSEC (DNS Security Extension) standards in the handling of DNS dynamic updates: 1) the on-line storage of a zone security key, creating a single point of attack for both inside and outside attackers, and 2) the violation of the role separation principle, which in the context of DNSSEC separates the roles of zone security managers from DNS server administrators. To address these issues, we propose a secure DNS architecture that is based on threshold cryptography. We show that the architecture adheres to the role separation principle without presenting any single point of attack. Our experimental results reveal that, in terms of signature computation times, our architecture incurs negligible performance penalty when using RSA/MD5 signatures but significant overhead when using DSA signatures. It is our belief that the high level of security that can be achieved by the proposed architecture far outweighs its potential overhead, especially in critical DNS zones, such as the .com zone.

Domain Name System (DNS) is the system for the mapping between easily memorizable host names and their IP addresses. Due to its criticality, security extensions to DNS have been proposed in an Internet Engineering Task Force (IETF) working group to provide authentication. In this paper, we point out two difficulties in the current DNSSEC (DNS Security Extension) standards in the handling of DNS dynamic updates: 1) the on-line storage of a zone security key, creating a single point of attack for both inside and outside attackers, and 2) the violation of the role separation principle, which in the context of DNSSEC separates the roles of zone security managers from DNS server administrators. To address these issues, we propose a secure DNS architecture that is based on threshold cryptography. We show that the architecture adheres to the role separation principle without presenting any single point of attack. Our experimental results reveal that, in terms of signature computation times, our architecture incurs negligible performance penalty when using RSA/MD5 signatures but significant overhead when using DSA signatures. It is our belief that the high level of security that can be achieved by the proposed architecture far outweighs its potential overhead, especially in critical DNS zones, such as the .com zone.

Most of the existing nonlinear data analysis and modelling techniques including neural networks become computationally prohibitively expensive when the available data set exceeds the capacity of the computer main memory due to the slow disc access operations [ l ] . For the data received on-line from a source with an unknown probability distribution, the question addressed in this article is how to eficiently partition it to smaller representative subsets {data bases) and how to organize these data subsets in order to minimize the computational cost of the later data analysis. The proposed linear-time, on-line problem decomposition method achieves these objectives through balancing probability distributions of the individual disjoint data subsets, each aimed at approximating the original data-source distribution. Consequently, computationally eficient statistical data analysis and neural network modelling on data subsets fitting into a computer central memory will produce results similar to these obtained through a global, computationally infeasible data analysis. In

Due to the dramatic increase of data volumes in different applications, it is becoming infeasible to keep these data in one centralized machine. It is becoming more and more natural to deal with distributed databases and networks. That is why distributed data mining techniques have been introduced. One of the most important data mining problems is data clustering. While many clustering algorithms exist for centralized databases, there is a lack of efficient algorithms for distributed databases. In this paper, an efficient algorithm is proposed for clustering distributed databases. The proposed methodology employs an iterative optimization technique to achieve better clustering objective. The experimental results reported in this paper show the superiority of the proposed technique over a recently proposed algorithm based on a distributed version of the well known K-Means algorithm (Datta et al. 2009) [1].

Peer-to-peer file-sharing networks are currently receiving much attention as a means of sharing and distributing information. However, as recent experience shows, the anonymous, open nature of these networks offers an almost ideal environment for the spread of self-replicating inauthentic files. We describe an algorithm to decrease the number of downloads of inauthentic files in a peer-to-peer file-sharing network that assigns each peer a unique global trust value, based on the peer's history of uploads. We present a distributed and secure method to compute global trust values, based on Power iteration. By having peers use these global trust values to choose the peers from whom they download, the network effectively identifies malicious peers and isolates them from the network. In simulations, this reputation system, called EigenTrust, has been shown to significantly decrease the number of inauthentic files on the network, even under a variety of conditions where malicious peers cooperate in an attempt to deliberately subvert the system.

The healthcare industry is undergoing a significant transformation in data
management, spurred by the integration of artificial intelligence (AI) and cloud
technologies in data warehousing. This paper investigates the transformative
potential of AI-driven Extract, Transform, Load (ETL) processes and cloud
integration within healthcare data warehouses. We explore how these
technologies address key challenges such as data integration, real-time
processing, and scalability, which are critical in healthcare environments. By
examining various applications and proposing an implementation framework,
this study provides a roadmap for optimizing healthcare data warehouses to
support enhanced patient care, operational efficiency, and advanced analytics
capabilities.

The insurance and risk management sectors are experiencing a significant transformation driven by
the need for more sophisticated data analysis and predictive modeling. This paper explores the critical role of
artificial intelligence (AI) in enhancing Extract, Transform, Load (ETL) processes for modernizing data
warehouses within these industries. We examine how AI-enhanced ETL addresses key challenges such as data
quality, integration of diverse data sources, and real-time processing. The study investigates various applications
and proposes a framework for successful implementation. Our findings suggest that AI-driven ETL offers
unprecedented opportunities for operational efficiency, improved risk assessment, and competitive advantage in
insurance and risk management.

Abstract- The healthcare industry is at a pivotal moment in terms of data management. Legacy data warehouses, which once
served the sector’s needs, are now proving inefficient in an era of rapid technological advancements. This article proposes a
framework for modernizing these legacy systems with Artificial Intelligence (AI) technologies, particularly AI-enhanced
Extract, Transform, Load (ETL) workflows. These technologies have the potential to significantly improve data quality,
operational efficiency, and scalability, especially in key areas such as Electronic Health Records (EHRs), Medical Imaging,
Hospital Management, and Medical Research. Additionally, AI enables predictive analytics, offering healthcare organizations
the ability to anticipate patient needs and optimize resource allocation. This paper explores the challenges healthcare
organizations face, the benefits of AI-driven solutions, and best practices for implementation.

Scientific workflow design is usually complex and demands integration of numerous resources. Geographical distribution and semantic heterogeneity of resources add to this complexity. The cost effectiveness of such workflow design thus depends upon the lifespan of the application and its anticipated use. Shorter application lifespan usually entails prohibitive development costs. In this paper, we present an alternative platform for declarative workflow design using BioFlow in such environments. We argue that a declarative workflow design using BioFlow is more efficient and cost effective compared to traditional approaches using systems such as Taverna. To demonstrate the advantages of BioFlow, we compare a canonical micro array data analysis workflow application design approach using both Taverna and BioFlow. We show that BioFlow supports ad hoc and modular application design at a throw away cost, and produces a superior maintainable application that can adapt to changes in the source without significant effort. Finally, we discuss a visual application builder, called VizBuilder, with which end users are able to design workflows without any knowledge of BioFlow.

Apache Hadoop, written in Java, is open-source framework that supports processing of large data sets. It can store a large volume of structured, semistructured, and unstructured data in a distributed file system and process them in parallel. Hadoop is composed of four major components: HDFS, MapReduce, YARN, and Hadoop Common.

In big data storage, architecture data reaches users through multiple organization data structures. The big data revolution provides significant improvements to the data storage architecture. New tools such as Hadoop, an open-source framework for storing data on clusters of commodity hardware, are developed, which allows organizations to effectively store and analyze large volumes of data. • Cluster computing is a distributed or parallel computing system comprising multiple stand-alone PCs connected together working as a single, integrated, highly available resource. • The benefits of massively parallel processors and cluster computers are high availability, scalable performance, fault tolerance, and the use of cost-effective commodity hardware. Scalability is achieved by removing nodes or adding additional nodes as per the demand without hindering the system's operation.

Sincere appreciation from my heart can not be conveyed in words to all the people who helped me carry out the research reported in this dissertation, by their support and encouragement, and to all those who have made my stay in France, the land of liberty, equality, and fraternity, an exciting and rewarding experience by their friendship and kindness. My sincere thanks to my advisors, Dr. Vincent Roca and Eng. Sébastien Loye. I am grateful for their advice, guidance, and patience. I am deeply indebted to Dr. Vincent Roca for giving me the chance to work with him, for his support, patience, and for all what I've learned from him. Many thanks also to Prof. Andrzej Duda the director of my thesis. All my gratitude to prof. Bernard Cousin and Dr. Isabelle Chrisment for the time they spent reading and commenting my thesis, and for the reports they wrote. And I also would like to thank Dr. Olivier Paul and Prof. Guy Mazaré for taking part in the committee of my thesis. I would like to express my immense appreciation to Dr. Renaud Moignard the head of the MTM (MPLS Traffic Engineering and Multicast) team at France Télécom R&D for his support during all this thesis period. I also wish to express appreciations to all my colleagues in the MTM team. A special thank to Mr. Jean-Luc Lehagre for his assistance in implementing the test-bed used in this study. I am grateful to Dr. Christian Guillemot, director of the CPN (Core Packet Network for NGN) laboratory for welcoming me at France Télécom R&D, and I would like to thank all the members of the CORE/CPN laboratory at Lannion they all helped me in a way or in another. I'm grateful to all the past and present members of the Planète team, for their support and helps. I was honored to work with them. I thank them sincerely for all the memorable moments, for their helps, and for their kindness. I would like to thank the many people who helped, advised, and gave fruitful discussions during this work. Many thanks to Eng. Jacqueline Boyer from France Télécom R&D at Lannion. I'm grateful for her help in the theoretical study presented in this thesis. I also would like to thank Dr. Mohammed Alchemlal from France Télécom R&D at Caen.

Los mapas auto-organizativos (SOM-Self organizing maps) son un tipo de red neuronal ampliamente conocido por su capacidad para preservar la topología de los datos de entrada permitiendo mapear un espacio n-dimensional en otro de dos dimensiones. Luego de entrenar la red, las neuronas competitivas que la forman agrupan la información disponible facilitando de esta forma la identificación de similitudes; esto ha motivado su estudio como base para la obtención de reglas de asociación y clasificación. Este artículo presenta un nuevo método capaz de obtener reglas de clasificación que operan tanto sobre atributos numéricos como nominales, denominado SOM+PSO, que combina una red SOM con una metaheurística basada en cúmulo de partículas (variante de PSO-Particle swarm optimization). El método propuesto ha sido comparado con PART y medido sobre 19 bases del repositorio UCI con resultados satisfactorios.

Interoperability plays an important role for a variety of applications. One of them are Peer Data Management Systems, where autonomous data sources (peers) interact with each other based on semantic mappings between their schemas. The building blocks that enable interoperability and thus the main challenges in such systems are mapping representation, query rewriting, and efficient query processing. While most approaches regard these aspects in separate this paper presents a comprehensive study of the interactions between these blocks. Our considerations try to provide a holistic view on semantic interoperability in distributed environments such as PDMS. We discuss techniques for distributed query processing and rewriting that consider high-level query operators such as top-N and skyline. Furthermore, we discuss how to increase efficiency by applying routing indexes and relaxation of result completeness/correctness.

In order to join two sub queries involving data from multiple sites data has to be transmitted from one site to other. While transmitting the data within a network, the factors involved in distributed databases is communication cost and amount of data transmitted. To minimize these factors, join operation is used. There are two cases considered in which query processing using join and query processing using semi join are described. The amount of data transfer in case of join is more than in case of semi join. Hence sub operations are executed dynamically to improve the communication cost. General Terms Distributed database, query processing, query optimization, data transmitted.

The vision of the upcoming 6G technologies that have fast data rate, low latency, and ultra-dense network, draws great attentions to the Internet of Vehicles (IoV) and Vehicle-to-Everything (V2X) communication for intelligent transportation systems. There is an urgent need for distributed machine learning techniques that can take advantages of massive interconnected networks with explosive amount of heterogeneous data generated at the network edge. In this study, a two-layer federated learning model is proposed to take advantages of the distributed end-edgecloud architecture typical in 6G environment, and to achieve a more efficient and more accurate learning while ensuring data privacy protection and reducing communication overheads. A novel multi-layer heterogeneous model selection and aggregation scheme is designed as a part of the federated learning process to better utilize the local and global contexts of individual vehicles and road side units (RSUs) in 6G supported vehicular networks. This context-aware distributed learning mechanism is then developed and applied to address intelligent object detection, which is one of the most critical challenges in modern intelligent transportation systems with autonomous vehicles. Evaluation results showed that the proposed method, which demonstrates a higher learning accuracy with better precision, recall and F1 score, outperforms other state-of-the-art methods under 6G network configuration by achieving faster convergence, and scales better with larger numbers of RSUs involved in the learning process.

The success of the World Wide Web has led to a steep increase in the user population and the amount of traffic on the Internet. Popular Web pages create "hot spots" of network load due to their great demand for bandwidth and increase the response time because of the overload on the Web servers. We propose the distribution of very popular and frequently changing Web documents using continuous multicast push (CMP). The benefits of CMP in the case of such documents are a very efficient use of network resources, a reduction of the load on the server, lower response times, and scalability for an increasing number of receivers. We present a quantitative evaluation of the continuous multicast push for a wide range of parameters. that change frequently, a continuous multicast push (CMP) can offer significant benefits in terms of load reduction, bandwidth savings, reduced response times, and scalability for the growing number of receivers. We view CMP as one of the three complementary delivery options integrated in a Web server: Unicast, AMP, and CMP: Unicast Pull: The document is sent as a response to a user's pull request. This delivery method is used for documents that are rarely requested. Asynchronous Multicast Push (AMP): Requests for the same document are accumulated over a small time interval, and answered together via multicast. This delivery method is used for popular documents that have multiple requests over limited period of time. Continuous Multicast Push (CMP): A document is continuously multicasted on the same multicast address. This delivery method is used for very popular documents that change very frequently and that are not worth caching. A Web server using CMP continuously multicasts the latest version of a popular Web document on a multicast address (every document is assigned a different multicast address). Receivers tune into the multicast group for the time required to reliably receive the document and then leave the group. In order to achieve a reliable document delivery, we propose the use of a forward error correction code (FEC) in combination with cyclic transmissions (see Section 3.4).

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Similarity search has been proved suitable for searching in very large collections of unstructured data objects. We are interested in efficient parallel query processing under situations of continuous streams of queries as in search engines. A number of sequential index data structures for this purpose have been proposed so far. This paper focuses on one representative of a class of these data structures, namely one based on clustering for which we evaluate different ways of distributing the index to support parallelism on a set of processors. Our study reveals that the intuitive method for both data distribution and model of computing are not efficient in practice. The best results are obtained with a strategy that appears to be more costly in construction but we show that in practice this cost is not significant.

We describe a case-study in which formal methods were used to verify an important responsiveness property of a distributed database system which is used heavily at Ericsson in a number of recent products. One of the aims of the project was to verify the actual running code which is written in the distributed functional language Erlang. In a joint project between SICS and Ericsson we have over the past few years been developing a tableau-based verification tool for Erlang of considerable scope. In particular, we are capable of addressing-on the level of running program code-systems with unbounded behaviour along the many dimensions in which this happens in "real" programs, involving datatypes, recursive control structures, error handling and recovery, initialisation, and dynamic process creation. The database lookup manager considered here contains most of these features, giving rise to infinite state behaviour which is not very adequately handled using model checking or other approaches based purely on state space traversal. In the paper we introduce the case study, our approach to formalisation and verification, and discuss our experiences using the Erlang verification tool.

Despite increasingly distributed internet information sources with diverse storage formats and access-control constraints, most of the end applications (e.g., filters and media players) that view and manipulate data from these sources operate against a traditional file-based interface. These legacy applications need to be rewritten to access remote sources, or need to rely upon ad hoc intermediary applications that aggregate the data into a passive file before executing the legacy application. This paper presents a simple, elegant, programmable method for allowing natural integration of legacy applications into distributed system infrastructures. The approach called active files, enables multiple information sources to be encapsulated as a local file that serves as their logical proxyw. This local file is accessed though a sentinel process, which automatically starts when the file is opened, aggregates data from multiple sources, and filters all access to and from the file. More importantly, the integration of active files into client applications is transparent: an active file is virtually indistinguishable from a regular file. Active files find a variety of applications in both distributed and nondistributed systems. We discuss active files, their semantics, their usage and their implementations in Windows NT.

It is well-known that fine tuning in database physical design is an important strategy for speeding up data access. In this paper, we introduce a new approach, denoted HypoPlans, to make relational database systems able to execute self-tuning actions, based on the notion of Hypothetical Query Execution Plans. HypoPlans is non-intrusive and completely autonomous. In this sense, it is DBMS-independent and does not require any DBA intervention. Our approach is based on heuristics that run continuously. Thus, HypoPlans is able to guide decisions on the current physical database configuration in order to dynamically react to workload changes. More specifically, we present in this paper the software architecture of a framework, which implements HypoPlans. In order to evaluate the viability of our approach, we have instantiated this framework for the database physical design concerning index (self)tuning. Our experiments show that HypoPlans is quite effective and efficient, also presenting low resource consumption.

Whereas many natural language researchers have identi ed ways in which natural language systems need to be augmented in order to make current natural language interactions more natural, most evaluations of natural language interfaces have failed to study natural language in the context of its use. Since there are now restricted natural language systems available commercially, we feel that it is critical to identify whether in fact these systems are of bene t to end-users and what bene ts these limited natural language systems o er. In this study, we have evaluated a restricted NL as an interface for database access, and compared it with another type of database interface medium, provided by a menu system. We feel that this is an important component of this study: evaluation cannot done in a vacuum, since for most information access applications there will be other types of interfaces available. We identi ed a group of users who made persistent use of the Natural Language system. It seemed that they were exploiting the fact that it allowed them to retrieve information at various levels of granularity, often by the use of coordinating expressions. They also seemed to take advantage of the increased exibility of expression provided by the NL interface, exempli ed by the use of ellipsis, and the capacity to vary the presentation of output. We also analysed the learnability of a restricted subset of Natural Language by examining the errors of both persistent and non-persistent users of the system. We discuss the implications of this analysis for determining those features of applications that make NL a suitable interface. We also comment on how users of NL systems might best be trained.

Data Fragments Allocation is an important issue in designing Distributed Database System. This problem is NP-complete, and thus requires fast heuristics and random algorithms to generate efficient solutions, so many algorithms for solving that have been reported in the literature. In this paper we used an object migration learning automaton-based algorithm. This approach is able to get suitable solutions in a reasonable amount of time even for moderate sized problems. Experimental results show that proposed algorithm has significant superiority over the several well-known methods.

The purpose of this study is to improve the web server performance. Bottlenecks such as network traffic overload and congested web server has still yet to be solved due to the increasing of internet usage. Caching is one of the popular and efficient solutions to reduce network latency. However, there is a need to study the current caching technique and further optimize the result. Therefore, this study proposed a Two-Tiered Caching System (2TCS). The 2TCS adapts a selective caching system concept which contains two tiers in its caching architecture. The 2TCS is used to further increase the probability of achieving a cache hit on top of utilizing the normal caching system. The proposed technique utilizes SQUID as its web cache server and also Mozilla Firefox's default caching system at the client side. A performance comparison of a normal caching system against the implementation of the 2TCS is made using the IBM Rational Performance Tester to prove the result.

As more data management software is designed for deployment in public and private clouds, or on a cluster of commodity servers, new distributed storage systems increasingly achieve high data access throughput via partitioning and replication. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. This article describes Calvin, a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. This allows near-linear scalability on a cluster of commodity machines, without eliminating traditional transactional guarantees, introducing a single point of failure, or requiring application developers to reason about data partitioning. By replicating transaction inputs instead of transactional actions, Calvin is able to support multiple consistency levels-including Paxos-based strong consistency across geographically distant replicas-at no cost to transactional throughput. Furthermore, Calvin introduces a set of tools that will allow application developers to gain the full performance benefit of Calvin's server-side transaction scheduling mechanisms without introducing the additional code complexity and inconvenience normally associated with using DBMS stored procedures in place of ad hoc client-side transactions.

As more data management software is designed for deployment in public and private clouds, or on a cluster of commodity servers, new distributed storage systems increasingly achieve high data access throughput via partitioning and replication. In order to achieve high scalability, however, today's systems generally reduce transactional support, disallowing single transactions from spanning multiple partitions. This article describes Calvin, a practical transaction scheduling and data replication layer that uses a deterministic ordering guarantee to significantly reduce the normally prohibitive contention costs associated with distributed transactions. This allows near-linear scalability on a cluster of commodity machines, without eliminating traditional transactional guarantees, introducing a single point of failure, or requiring application developers to reason about data partitioning. By replicating transaction inputs instead of transactional actions, Calvin is able to support ...

Storage Area Networks (SAN) are based on direct interaction between clients and storage servers. This unmediated access exposes the storage server to network attacks, necessitating a verification, by the server, that the client requests conform with the system protection policy. Solutions today can only enforce access control at the granularity of entire storage servers. This is an outcome of the way storage servers abstract storage: an array of fixed size blocks. Providing access control at the granularity of blocks is infeasible-there are too many active blocks in the server. Thus, the coarse granularity of entire servers is used. Object stores (e.g., the NASD system [10]) on the other hand provide means to address these issues. An object store control unit presents an abstraction of a dynamic collection of objects, each can be seen as a different array of blocks, thus providing the basis for protection at the object level. In this paper we present a security model for the object store which leverages on existing security infrastructure. We give a simple generic mechanism capable of enforcing an arbitrary access control policy at object granularity. This mechanism is specifically designed to achieve low overhead by minimizing the cost of validating an operation along the critical data path, and lends itself for optimizations such as caching. The key idea of the model is to separate the mechanisms for transport security from the one used for access control and to maximize the use standard security protocols when possible. We utilize a standard industry protocol for authentication, integrity and privacy on the communication channel (IPSec for IP networks) and define a proprietary protocol for authorization on top of the secure communication layer.

Workload consolidation, sharing physical resources among multiple workloads, is a promising technique to save cost and energy in cluster computing systems. This paper highlights a few challenges of workload consolidation for Hadoop as one of the current state-of-the-art data-intensive cluster computing system. Through a systematic step-by-step procedure, we investigate challenges for efficient server consolidation in Hadoop environments. To this end, we first investigate the inter-relationship between last level cache (LLC) contention and throughput degradation for consolidated workloads on a single physical server employing Hadoop distributed file system (HDFS). We then investigate the general case of consolidation on multiple physical servers so that their throughput never falls below a desired/predefined utilization level. We use our empirical results to model consolidation as a classic two-dimensional bin packing problem and then design a computationally efficient greedy algorithm to achieve minimum throughput degradation on multiple servers. Results are very promising and show that our greedy approach is able to achieve near optimal solution in all experimented cases.

Remote data integrity checking is of crucial importance in cloud storage. It can make the clients verify whether their outsourced data is kept intact without downloading the whole data. In some application scenarios, the clients have to store their data on multi-cloud servers. At the same time, the integrity checking protocol must be efficient in order to save the verifier's cost. From the two points, we propose a novel remote data integrity checking model: ID-DPDP (identity-based distributed provable data possession) in multi-cloud storage. The formal system model and security model are given. Based on the bilinear pairings, a concrete ID-DPDP protocol is designed. The proposed ID-DPDP protocol is provably secure under the hardness assumption of the standard CDH (computational Diffie-Hellman) problem. In addition to the structural advantage of elimination of certificate management, our ID-DPDP protocol is also efficient and flexible. Based on the client's authorization, the proposed ID-DPDP protocol can realize private verification, delegated verification and public verification.

A real-time database is a database in which both the data and the operations upon the data may have timing constraints. The design of this kind of database requires the introduction of new concepts to modelize both data structures and the dynamic behavior of the database. In this paper, we propose an UML2.O profile, entitled UML-RTDB, allowing the design of structural model for a real-time database. One of the main advantages of UML-RTDB is its capacity to take into account real-time database properties through specialized concepts in rigourous, easy and expressive manner.

While simulation is a valuable research and design tool, the time and difficulty required to create new simulations (or re-use existing simulations) often limits their application. This report describes the design of the software architecture for the Reconfigurable Flight Simulator (RFS), which provides a robust simulation framework that allows the simulator to fulfill multiple research and development goals. The core of the architecture provides the interface standards for simulation components, registers and initializes components, and handles the communication between simulation components. The simulation components are each a pre-compiled library 'plugin' module. This modularity allows independent development and sharing of individual simulation components. Additional interfaces can be provided through the use of Object Data/Method Extensions (OD/ME). RFS provides a programmable run-time environment for real-time access and manipulation, and has networking capabilities using the High Level Architecture (HLA).

Lafayette, IN, a position he has held since 1976. His research interests include algorithms for data storage and retrieval, programming languages, and software engineering. Dr. Comer is a member of the Association for Computing Machinery and Sigma Xi.

Due to the dramatic increase of data volumes in different applications, it is becoming infeasible to keep these data in one centralized machine. It is becoming more and more natural to deal with distributed databases and networks. That is why distributed data mining techniques have been introduced. One of the most important data mining problems is data clustering. While many clustering algorithms exist for centralized databases, there is a lack of efficient algorithms for distributed databases. In this paper, an efficient algorithm is proposed for clustering distributed databases. The proposed methodology employs an iterative optimization technique to achieve better clustering objective. The experimental results reported in this paper show the superiority of the proposed technique over a recently proposed algorithm based on a distributed version of the well known K-Means algorithm (Datta et al. 2009) [1].

Laboratorio de Investigación y Desarrollo en Inteligencia Artificial (LIDIA) Dep. de Ciencias e Ingenieŕıa de la Computación, Universidad Nacional del Sur (UNS), Instituto de Ciencias e Ingenieŕıa de la Computación (UNS-CONICET), Consejo Nacional de Investigaciones Cient́ıficas y Técnicas (CONICET). Av. Alem 1253, (B8000CPB) Bah́ıa Blanca, Argentina Laboratorio de Investigación y Desarrollo en Inteligencia Artificial Concordia Facultad de Ciencias de la Administración, Universidad Nacional de Entre Ŕıos, e-mail: {jct,sg,ajg,grs}@cs.uns.edu.ar

1 Laboratorio de Investigación y Desarrollo en Inteligencia Artificial (LIDIA), Depto. de Ciencias e Ingenieŕıa de la Computación, Universidad Nacional del Sur, Bah́ıa Blanca, Argentina. 2 Consejo Nacional de Investigaciones Cient́ıficas y Técnicas (CONICET), Buenos Aires, Argentina. 3 Depto. de Matemática, Universidad Nacional de Santiago del Estero, Santiago del Estero, Argentina. e-mail: {mlc,mcdb,sag,dcm,grs}@cs.uns.edu.ar

We describe ongoing efforts towards developing language resources for a transnational digital government project aimed at applying information technology (IT) to a problem of international concern: detecting and monitoring activities related to the transnational movement of illicit drugs. The project seeks to support information sharing, coordination and collaboration among government agencies within a country and across national boundaries by combining a variety of technologies including a distributed query processor with form-based and conversational user interfaces, a language translation system, an event server for event filtering and notification, and an event-trigger-rule server. The prototype system is being developed by U.S. universities in collaboration with an international agency and with universities and government agencies in Belize and the Dominican Republic. This paper focuses on the linguistic resources and their use in Example-Based Machine Translation (EBMT). We are in the process of developing an English-Spanish parallel corpus, focused on the domain of information elicited and used at border crossings, to fuel the EBMT system. While significant parallel corpora are available for these two languages in the newswire domain, they were found to be of very limited use for the border crossings application, spurring the need to develop our own resources.

OLAP tools for distributed data warehouses generally assume underlying replicated tables are up to date. Unfortunately, maintaining updated replicas is difficult due to the inherent tradeoff between consistency and availability. In this paper, we propose techniques to evaluate OLAP queries in distributed data warehouses assuming a lazy replication model. Considering that it may be admissible to evaluate OLAP queries with slightly outdated replicated tables, our technique first efficiently computes the degree of obsolescence of replicated local tables and when such result is acceptable, given an error threshold, then the query is evaluated locally, avoiding the transmission of large tables over the network. Otherwise, the query can be remotely evaluated less efficiently with the master copy of tables, provided they are stored at a single site. Inconsistency measurement is computed by adapting distributed set reconciliation algorithms to efficiently compute the symmetric difference between the master and replicated tables. Our improved distributed database algorithm has linear communication complexity and cubic time complexity in the size of the symmetric difference, which is expected to be small in a replicated data warehouse. Our technique is independent of the method employed to propagate data warehouse insertions, deletions and updates. We present experiments simulating distributed databases, with different CPU and transmission speeds, showing our method is effective to decide if the query should be evaluated either locally or remotely.

The success of the World Wide Web has led to a steep increase in the user population and the amount of traffic on the Internet. Popular Web pages create "hot spots" of network load due to their great demand for bandwidth and increase the response time because of the overload on the Web servers. We propose the distribution of very popular and frequently changing Web documents using continuous multicast push (CMP). The benefits of CMP in the case of such documents are a very efficient use of network resources, a reduction of the load on the server, lower response times, and scalability for an increasing number of receivers. We present a quantitative evaluation of the continuous multicast push for a wide range of parameters. that change frequently, a continuous multicast push (CMP) can offer significant benefits in terms of load reduction, bandwidth savings, reduced response times, and scalability for the growing number of receivers. We view CMP as one of the three complementary delivery options integrated in a Web server: Unicast, AMP, and CMP: Unicast Pull: The document is sent as a response to a user's pull request. This delivery method is used for documents that are rarely requested. Asynchronous Multicast Push (AMP): Requests for the same document are accumulated over a small time interval, and answered together via multicast. This delivery method is used for popular documents that have multiple requests over limited period of time. Continuous Multicast Push (CMP): A document is continuously multicasted on the same multicast address. This delivery method is used for very popular documents that change very frequently and that are not worth caching. A Web server using CMP continuously multicasts the latest version of a popular Web document on a multicast address (every document is assigned a different multicast address). Receivers tune into the multicast group for the time required to reliably receive the document and then leave the group. In order to achieve a reliable document delivery, we propose the use of a forward error correction code (FEC) in combination with cyclic transmissions (see Section 3.4).

Data warehouses are nowadays an important component in every competitive system, it's one of the main components on which business intelligence is based. We can even say that many companies are climbing to the next level and use a set of Data warehouses to provide the complete information or it's generally due to fusion of two or many companies. these Data warehouses can be heterogeneous and geographically separated , this structure is what we call federation, and even if the components are physically separated, they are logically seen as a single component. generally, these items are heterogeneous which make it difficult to create the logical federation schema ,and the execution of user queries a complicated mission. In this paper, we will fill this gap by proposing an extension of an existent algorithm in order to treat different schema types (star , snow flack) including the treatment of hierarchies dimension using ontology

Resumen. El diseño de bases de datos distribuidas es un problema de optimización que implica la solución de problemáticas como la fragmentación de los datos y su ubicación. Típicamente, los criterios que determinan si la fragmentación y la asignación son óptimas se establecen de manera independiente. Primero se busca la "mejor" fragmentación y luego la "mejor" ubicación de los fragmentos obtenidos. La fragmentación vertical es más complicada que la partición horizontal, debido al incremento del número de posibles alternativas. En este trabajo se presenta un nuevo método para la fragmentación vertical, que se basa fundamentalmente en la Matriz de Atracción entre Atributos, suplantando la conocida Matriz de Afinidad entre Atributos. Se utiliza como heurística el enfoque de agrupamientos jerárquicos y una regla de decisión basada en la homogeneidad interna y la heterogeneidad externa de los grupos obtenidos. También se presenta una variante para que pueda ser usado como evaluador de particiones. Palabras claves: Bases de datos distribuidas, evaluador de particiones, fragmentación vertical, medida de afinidad, método jerárquico, regla de decisión.

El diseño de bases de datos distribuidas es un problema de optimización que implica la solución de problemáticas como la fragmentación de los datos y su ubicación. Típicamente, los criterios que determinan si la fragmentación y la asignación son óptimas se establecen de manera independiente. Primero se busca la "mejor" fragmentación y luego la "mejor" ubicación de los fragmentos obtenidos. La fragmentación vertical es más complicada que la partición horizontal, debido al incremento del número de posibles alternativas. En este trabajo se presenta un nuevo método para la fragmentación vertical, que se basa fundamentalmente en la Matriz de Atracción entre Atributos, suplantando la conocida Matriz de Afinidad entre Atributos. Se utiliza como heurística el enfoque de agrupamientos jerárquicos y una regla de decisión basada en la homogeneidad interna y la heterogeneidad externa de los grupos obtenidos. También se presenta una variante para que pueda ser usado como evaluador de particiones. Palabras clave: Bases de datos distribuidas, evaluador de particiones, fragmentación vertical, medida de afinidad, regla de decisión.

Current DoD enterprise networks routinely face targeted cyber attacks, and even though attack-related information is recorded in various places, this information is often left unexamined until after attacker objectives have been achieved. This is especially true for large networks consisting of continuously changing networked devices, including laptops, servers, printers, IP phones, and more. This paper describes the design of Gestalt, a next-generation cyber information management platform that simplifies access to cyber event data stored in the nooks and crannies of a distributed enterprise. The ready and secure access to cyber information provided by Gestalt is a key enabler for a new set of techniques that can detect and mitigate targeted cyber attacks within hours instead of months. Current state-of-the-art approaches to automated and operator assisted cyber defense are ill-suited to counter targeted cyber attacks because these technologies (1) focus only on aggregated one-dimensional features across multiple devices, (2) do not provide the required coverage over all networked devices and observables accessible on those devices, and (3) lack the expressiveness and deeper semantic backing required to detect targeted attacks across a sea of lowlevel observables. Gestalt provides innovations in (1) automatically discovering devices and useful data sources in the enterprise (beyond simple IP connectivity), (2) maintaining a metadata index of devices and observable information (even of devices without schemas and connectors), and (3) transparently decomposing and federating semantic graph queries to devices (rather than extracting and aggregating information in a central store), and integrating the results back into a well-defined ontology.

Supply chains (SC) span many geographies, modes and industries and involve several phases where data flows in both directions from suppliers, manufacturers, distributors, retailers, to customers. This data flow is necessary to support critical business decisions that may impact product cost and market share. Current SC information systems are unable to provide validated, pseudo real-time shipment tracking during the distribution phase. This information is available from a single source, often the carrier, and is shared with other stakeholders on an as-needed basis. This paper introduces an independent, crowd-validated, online shipment tracking framework that complements current enterprise-based SC management solutions. The proposed framework consists of a set of private distributed ledgers and a single blockchain public ledger. Each private ledger allows the private sharing of custody events among the trading partners in a given shipment. Privacy is necessary, for example, when trading high-end products or chemical and pharmaceutical products. The second type of ledger is a blockchain public ledger. It consists of the hash code of each private event in addition to monitoring events. The latter provide an independently validated immutable record of the pseudo real-time geolocation status of the shipment from a large number of sources using commuters-sourcing.