Blockchain databases were designed to improve trust in centralized ecosystems, which dominate the market today, by introducing tamper-evidence features on top of a classical database. Compared to decentralized ledger technologies, blockchain databases are easier to use, and they can significantly reduce the operational and development costs. However, the existing blockchain databases do not equip multiple parties with tools to efficiently control common data written to the ledger. This paper describes Orion, a new open source blockchain database that introduces multi-sig and proof capabilities with extensive key-level access control, which enable parties to mutually control and validate a value written to the database. These unique capabilities, together with additional blockchain properties, provide users with features such as tamper-evidence, provenance, data lineage, authenticity, and non-repudiation, while using a standard data model and transactional APIs. We found our technology to be extremely useful in improving the integrity of a system and reducing mistakes, disputes, and fraud.

The Hyperledger Fabric is well known and the most prominent enterprise-grade permissioned blockchain. The architecture of the Hyperledger Fabric introduces a new architecture paradigm of simulate-order-validate and pluggable architecture, allowing a greater level of customization where one of the critical components is the world state database, which is responsible for capturing the snapshot of the blockchain application state. Hyperledger Fabric manages the state with the key-value database abstraction and peer updates it after transactions have been validated and read from the state during simulation. Therefore, providing good performance during reading and writing impacts the system's overall performance. Currently, Hyperledger Fabric supports two different implementations of the state database. One is LevelDB, the embedded DB based on LSM trees and CouchDB. In this study, we would like to focus on searching and exploring the alternative implementation of a state database and analyze whenever there are better and more scalable options. We evaluated different databases to be plugged into Hyperledger Fabric, such as RocksDB, Boltdb, and BadgerDB. The study describes how to plug new state databases and performance results based on various workloads.

Introduction. The execute-order-validate blockchain architecture, introduced by Hyperledger Fabric (HLF) [1], allows parallel transactions execution and validation, thus raising the need for transaction isolation. Today transaction isolation is attained by locking the entire state database (DB) during simulation of transactions and state update. This lock is one of the major bottlenecks as observed by previous work [3]. This work presents a new lock-free approach for providing transaction isolation.

Fabric is a modular and extensible open-source system for deploying and operating permissioned blockchains and one of the Hyperledger projects hosted by the Linux Foundation (www.hyperledger.org).

No abstract available.

Distributed Ledger Technology (DLT) is rapidly emerging as a new paradigm for automating complex business processes in secure and decentralised fashion. Currently, however, its wider adoption is hampered by scalability problems [3] rooted in an inherent tension between stringent consistency, security, and robustness requirements on one hand, and growing application demand coupled with high performance expectations on the other. For example, popular peer-to-peer DLTs based on proof-of-work consensus [4] can only improve the transaction throughput by degrading their security and consistency guarantees, which is unacceptable in the enterprise and mission-critical settings.

We provide the first streaming algorithm for computing a provable approximation to the k-means of sparse Big Data. Here, sparse Big Data is a stream of n vectors in ℝd, where each vector has O(1) non-zeroes entries and possibly d ≥ n. E.g., adjacency matrix of a graph, web-links, social network, document-terms, or image-features matrices. Our streaming algorithm stores at most log n · kO(1) input points in memory. If the stream is distributed among M machines, the running time reduces by a factor of M, while communicating a total of M · kO(1) (sparse) input points between the machines. Our main contribution is a deterministic algorithm for computing a sparse (k, ∊)-coreset, which is a weighted subset of kO(1) input points that approximates the sum of squared distances from the n input points to every set of k centers, up to (1 ± ∊) factor, for any given constant ε > 0. This is the first such coreset of size independent of both d and n. Our experimental results show how our algorithm can bs used to boost the performance of any given k-means heuristics, even in the off-line setting. Open access to our implementation is also provided.

A proliferation of travel-related web sites enable people to share their travel plans, review hotels, offer advice, and more. In this paper we study social travel planning in the enterprise. While business travelers and leisure travelers have different preferences and needs, employees may benefit from sharing information and travel plans within the enterprise. We present a study collecting the requirements for social travel from employees, detail the design principles of a social travel application in the enterprise, and present Voyage, the outcome. We evaluated Voyage based on qualitative and quantitative data and discuss the results using four perspectives: collaborative activities, social information impact, usage patterns, and sharing behavior. Employees expressed their growing satisfaction from the social information contributed by fellow employees. Moreover, we observed that Voyage shortens the reservation time, thus saving costs for the enterprise.