An incremental database access method for autonomous interoperable databases

We investigated a number of design and performance issues of interoperable database management systems (DBMS's). The major results of our investigation were obtained in the areas of client-server database architectures for heterogeneous DBMS's, incremental computation models, buffer management techniques, and query optimization. We finished a prototype of an advanced client-server workstation-based DBMS which allows access to multiple heterogeneous commercial DBMS's. Experiments and simulations were then run to compare its performance with the standard client-server architectures. The focus of this research was on adaptive optimization methods of heterogeneous database systems. Adaptive buffer management accounts for the random and object-oriented access methods for which no known characterization of the access patterns exists. Adaptive query optimization means that value distributions and selectives, which play the most significant role in query plan evaluation, are continuously refined to reflect the actual values as opposed to static ones that are computed off-line. Query feedback is a concept that was first introduced to the literature by our group. We employed query feedback for both adaptive buffer management and for computing value distributions and selectivities. For adaptive buffer management, we use the page faults of prior executions to achieve more 'informed' management decisions. For the estimation of the distributions of the selectivities, we use curve-fitting techniques, such as least squares and splines, for regressing on these values

A Survey on EOSIO Systems Security: Vulnerability, Attack, and Mitigation

EOSIO, as one of the most representative blockchain 3.0 platforms, involves lots of new features, e.g., delegated proof of stake consensus algorithm and updatable smart contracts, enabling a much higher transaction per second and the prosperous decentralized applications (DApps) ecosystem. According to the statistics, it has reached nearly 18 billion USD, taking the third place of the whole cryptocurrency market, following Bitcoin and Ethereum. Loopholes, however, are hiding in the shadows. EOSBet, a famous gambling DApp, was attacked twice within a month and lost more than 1 million USD. No existing work has surveyed the EOSIO from a security researcher perspective. To fill this gap, in this paper, we collected all occurred attack events against EOSIO, and systematically studied their root causes, i.e., vulnerabilities lurked in all relying components for EOSIO, as well as the corresponding attacks and mitigations. We also summarized some best practices for DApp developers, EOSIO official team, and security researchers for future directions.Comment: 34 pages, 12 figure

Transactional Client-Server Cache Consistency: Alternatives and Performance

Client-server database systems based on a page server model can exploit client memory resources by caching copies of pages across transaction boundaries. Caching reduces the need to obtain data from servers or other sites on the network. In order to ensure that such caching does not result in the violation of transaction semantics, a cache consistency maintenance algorithm is required. Many such algorithms have been proposed in the literature and, as all provide the same functionality, performance is a primary concern in choosing among them. In this paper we provide a taxonomy that describes the design space for transactional cache consistency maintenance algorithms and show how proposed algorithms relate to one another. We then investigate the performance of six of these algorithms, and use these results to examine the tradeoffs inherent in the design choices identified in the taxonomy. The insight gained in this manner is then used to reflect upon the characteristics of other algorithms that have been proposed. The results show that the interactions among dimensions of the design space can impact performance in many ways, and that classifications of algorithms as simply Pessimistic" or Optimistic" do not accurately characterize the similarities and differences among the many possible cache consistency algorithms. (Also cross-referenced as UMIACS-TR-95-84

Cloud transactions and caching for improved performance in clouds and DTNs

In distributed transactional systems deployed over some massively decentralized cloud servers, access policies are typically replicated. Interdependencies ad inconsistencies among policies need to be addressed as they can affect performance, throughput and accuracy. Several stringent levels of policy consistency constraints and enforcement approaches to guarantee the trustworthiness of transactions on cloud servers are proposed. We define a look-up table to store policy versions and the concept of Tree-Based Consistency approach to maintain a tree structure of the servers. By integrating look-up table and the consistency tree based approach, we propose an enhanced version of Two-phase validation commit (2PVC) protocol integrated with the Paxos commit protocol with reduced or almost the same performance overhead without affecting accuracy and precision. A new caching scheme has been proposed which takes into consideration Military/Defense applications of Delay-tolerant Networks (DTNs) where data that need to be cached follows a whole different priority levels. In these applications, data popularity can be defined not only based on request frequency, but also based on the importance like who created and ranked point of interests in the data, when and where it was created; higher rank data belonging to some specific location may be more important though frequency of those may not be higher than more popular lower priority data. Thus, our caching scheme is designed by taking different requirements into consideration for DTN networks for defense applications. The performance evaluation shows that our caching scheme reduces the overall access latency, cache miss and usage of cache memory when compared to using caching schemes --Abstract, page iv

A speculative execution approach to provide semantically aware contention management for concurrent systems

PhD ThesisMost modern platforms offer ample potention for parallel execution of concurrent programs yet concurrency control is required to exploit parallelism while maintaining program correctness. Pessimistic con- currency control featuring blocking synchronization and mutual ex- clusion, has given way to transactional memory, which allows the composition of concurrent code in a manner more intuitive for the application programmer. An important component in any transactional memory technique however is the policy for resolving conflicts on shared data, commonly referred to as the contention management policy. In this thesis, a Universal Construction is described which provides contention management for software transactional memory. The technique differs from existing approaches given that multiple execution paths are explored speculatively and in parallel. In the resolution of conflicts by state space exploration, we demonstrate that both concur- rent conflicts and semantic conflicts can be solved, promoting multi- threaded program progression. We de ne a model of computation called Many Systems, which defines the execution of concurrent threads as a state space management problem. An implementation is then presented based on concepts from the model, and we extend the implementation to incorporate nested transactions. Results are provided which compare the performance of our approach with an established contention management policy, under varying degrees of concurrent and semantic conflicts. Finally, we provide performance results from a number of search strategies, when nested transactions are introduced

A Framework for Discovery and Diagnosis of Behavioral Transitions in Event-streams

Date stream mining techniques can be used in tracking user behaviors as they attempt to achieve their goals. Quality metrics over stream-mined models identify potential changes in user goal attainment. When the quality of some data mined models varies significantly from nearby models—as defined by quality metrics—then the user’s behavior is automatically flagged as a potentially significant behavioral change. Decision tree, sequence pattern and Hidden Markov modeling being used in this study. These three types of modeling can expose different aspect of user’s behavior. In case of decision tree modeling, the specific changes in user behavior can automatically characterized by differencing the data-mined decision-tree models. The sequence pattern modeling can shed light on how the user changes his sequence of actions and Hidden Markov modeling can identifies the learning transition points. This research describes how model-quality monitoring and these three types of modeling as a generic framework can aid recognition and diagnoses of behavioral changes in a case study of cognitive rehabilitation via emailing. The date stream mining techniques mentioned are used to monitor patient goals as part of a clinical plan to aid cognitive rehabilitation. In this context, real time data mining aids clinicians in tracking user behaviors as they attempt to achieve their goals. This generic framework can be widely applicable to other real-time data-intensive analysis problems. In order to illustrate this fact, the similar Hidden Markov modeling is being used for analyzing the transactional behavior of a telecommunication company for fraud detection. Fraud similarly can be considered as a potentially significant transaction behavioral change

A Framework for Data Sharing in Computer Supported Cooperative Environments

Concurrency control is an indispensable part of any information sharing system. Co-operative work introduces new requirements for concurrency control which cannot be met using existing applications and database management systems developed for non-cooperative environments. The emphasis of concurrency control in conventional database management systems is to keep users and their applications from inadvertently corrupting data rather than support a workgroup develop a product together. This insular approach is necessary because applications that access the database have been built with the assumptions that they have exclusive access to the data they manipulate and that users of these applications are generally oblivious of one another. These assumptions, however, are counter to the premise of cooperative work in which human-human interaction is emphasized among a group of users utilizing multiple applications to jointly accomplish a common goal. Consequently, applying conventional approaches to concurrency control are not only inappropriate for cooperative data sharing but can actually hinder group work. Computer support for cooperative work must therefore adopt a fresh approach to concurrency control which does promote group work as much as possible, but without sacrifice of all ability to guarantee system consistency. This research presents a new framework to support data sharing in computer supported cooperative environments; in particular, product development environments where computer support for cooperation among distributed and diverse product developers is essential to boost productivity. The framework is based on an extensible object-oriented data model, where data are represented as a collection of interrelated objects with ancillary attributes used to facilitate cooperation. The framework offers a flexible model of concurrency control, and provides support for various levels of cooperation among product developers and their applications. In addition, the framework enhances group activity by providing the functionality to implement user mediated consistency and to track the progress of group work. In this dissertation, we present the architecture of the framework; we describe the components of the architecture, their operation, and how they interact together to support cooperative data sharing

Tourism Portal

In the flexibility of the uses the interface has been developed a graphics concept in mind, associated through a browses interface. The GUI’S at the top level have been categorized as 1. Administrative user interface 2. The operational or generic user interface. The administrative user interface concentrates on the consistent information that is practically, part of the organizational activities and which needs proper authentication for the data collection. The interfaces help the administrations with all the transactional states like Data insertion, Data deletion and Date updation along with the extensive data search capabilities. The operational or generic user interface helps the users upon the system in transactions through the existing data and required services. The operational user interface also helps the ordinary users in managing their own information in a customized manner as per the assisted flexibilities

Power efficiency through tuple ranking in wireless sensor network monitoring

In this paper, we present an innovative framework for efficiently monitoring Wireless Sensor Networks (WSNs). Our framework, coined KSpot, utilizes a novel top-k query processing algorithm we developed, in conjunction with the concept of in-network views, in order to minimize the cost of query execution. For ease of exposition, consider a set of sensors acquiring data from their environment at a given time instance. The generated information can conceptually be thought as a horizontally fragmented base relation R. Furthermore, the results to a user-defined query Q, registered at some sink point, can conceptually be thought as a view V . Maintaining consistency between V and R is very expensive in terms of communication and energy. Thus, KSpot focuses on a subset V′ (⊆ V ) that unveils only the k highest-ranked answers at the sink, for some user defined parameter k. To illustrate the efficiency of our framework, we have implemented a real system in nesC, which combines the traditional advantages of declarative acquisition frameworks, like TinyDB, with the ideas presented in this work. Extensive real-world testing and experimentation with traces from University of California-Berkeley, the University of Washington and Intel Research Berkeley, show that KSpot provides an up to 66% of energy savings compared to TinyDB, minimizes both the size and number of packets transmitted over the network (up to 77%), and prolongs the longevity of a WSN deployment to new scales