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Recently the long standing problem of optimal construction of quantile sketches was resolved by K arnin, L ang, and L iberty using the KLL sketch (FOCS 2016). The algorithm for KLL is restricted to online insert operations and no delete operations. For many real-world applications, it is necessary to support delete operations. When the data set is updated dynamically, i.e., when data elements are inserted and deleted, the quantile sketch should reflect the changes. In this paper, we propose KLL ± , the first quantile approximation algorithm to operate in the bounded deletion model to account for both inserts and deletes in a given data stream. KLL ± extends the functionality of KLL sketches to support arbitrary updates with small space overhead. The space bound for KLL ± is [EQUATION], where ∈ and δ are constants that determine precision and failure probability, and α bounds the number of deletions with respect to insert operations. The experimental evaluation of KLL ± highlights that with minimal space overhead, KLL ± achieves comparable accuracy in quantile approximation to KLL. 

Significant amounts of data are currently being stored and managed on third-party servers. It is impractical for many small scale enterprises to own their private datacenters, hence renting third-party servers is a viable solution for such businesses. But the increasing number of malicious attacks, both internal and external, as well as buggy software on third-party servers is causing clients to loose their trust in these external infrastructures. While small enterprises cannot avoid using external infrastructures, they need the right set of protocols to manage their data on untrusted infrastructures. In this paper, we propose TFCommit, a novel atomic commitment protocol that executes transactions on data stored across multiple untrusted servers. To our knowledge, TFCommit is the first atomic commitment protocol to execute transactions in an untrusted environment without using expensive Byzantine replication. Using TFCommit, we propose an auditable data management system, Fides, residing completely on untrustworthy infrastructure. As an auditable system, Fides guarantees the detection of potentially malicious failures occurring on untrusted servers using tamper-resistant logs with the support of cryptographic techniques. The experimental evaluation demonstrates the scalability of our approach and the relatively low overhead of executing transactions on untrusted infrastructure. 

The uprise of Bitcoin and other peer-to-peer cryptocurrencies has opened many interesting and challenging problems in cryptography, distributed systems, and databases. The main underlying data structure is blockchain, a scalable fully replicated structure that is shared among all participants and guarantees a consistent view of all user transactions by all participants in the system. In this tutorial, we discuss the basic protocols used in blockchain, and elaborate on its main advantages and limitations. To overcome these limitations, we provide the necessary distributed systems background in managing large scale fully replicated ledgers, using Byzantine Agreement protocols to solve the consensus problem. Finally, we expound on some of the most recent proposals to design scalable and efficient blockchains in both permissionless and permissioned settings. The focus of the tutorial is on the distributed systems and database aspects of the recent innovations in blockchains 

CockroachDB is an open-source database, providing transactional access to data in a distributed setting. CockroachDB employs a multi-version timestamp ordering protocol to provide serializability. This provides a simple mechanism to enforce serializability, but the static timestamp allocation scheme can lead to a high number of aborts under contention. We aim to reduce the aborts for transactional workloads by integrating a dynamic timestamp ordering based concurrency control scheme in CockroachDB. Dynamic timestamp ordering scheme tries to reduce the number of aborts by allocating timestamps dynamically based on the conflicts of accessed data items. This gives a transaction higher chance to fit on a logically serializable timeline, especially in workloads with high contention.