non-type template parameter74 Conclusions Zero-cost abstractions Encapsulation Inheritance Dynamic Polymorphism Static Polymorphism Negative-cost abstractions More C++ for the embedded world! Architecture

Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/23: Cardinality and frequency estimation ??? Vasiliki Kalavri | Boston University 2020 Counting distinct elements 2 ??? Vasiliki Kalavri Boston University 2020 26 • Query approximation error • Error probability Guarantee: The estimation error for frequencies will not exceed with probability • A higher number of hash functions 2003. • Flajolet, Philippe, et al. Hyperloglog: the analysis of a near-optimal cardinality estimation algorithm. 2007. https://hal.archives-ouvertes.fr/file/index/docid/406166/ filename/FlFuGaMe07

managers need to work with stakeholders representing the requirements, systems engineering, contracting, cost estimating, and testing communities to design processes around short releases. Acquisition executives a contract strategy, shaping systems engineering processes, managing requirements, and developing cost estimates for programs with a dynamic scope. Experience indicates that cultural changes must .................................................................................. 33 11 Cost Estimation ............................................................................................

θ2x2 + θ3x3 + θ4x4 To eliminate the influence of θ3x3 and θ4x4 to smoothen hypothesis function, the cost function can be modified as follows min θ 1 2m � m � i=1 (hθ(x(i)) − y(i))2 + 1000 · θ2 3 + 1000 As the magnitudes of the fitting parameters increase, there will be an increasing penalty on the cost function This penalty is dependent on the squares of the parameters as well as the magnitude of λ Regularization and Bayesian Statistics September 20, 2023 9 / 25 Regularized Logistic Regression Recall the cost function for logistic regression J(θ) = − 1 m m � i=1 [y(i) log(hθ(x(i))) + (1 − y(i)) log(1 −

Debian had been developed using traditional proprietary methods, the COCOMO model estimates that its cost would be close to $6.1 billion USD to develop Debian 3.0. In addition, we offer both an analysis of [Robles2004]). 4.4 Effort and cost estimations Using the basic COCOMO model [Boehm1981], the effort to build a system with the same size as Debian 3.0 can be estimated. This estimation assumes a “classical”, (KSLOC**1.05) • Estimated schedule: 81.72 months (6.81 years) Formula: 2.5 * (Effort**0.38) • Estimated cost to develop: 3,625,000,000 USD To get these figures, each project was estimated as though it was developed

in another city. This solution has the disadvantages of low resource utilization, high maintenance cost, and the fact that RTO (Recovery Time Objective) and RPO (Recovery Point Objective) cannot meet expectations The solution is to use sharding middleware or a NewSQL database (like TiDB), and the latter is more cost-effective. TiDB adopts a separate computing and storage architecture, which enables you to scale out With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary

in another city. This solution has the disadvantages of low resource utilization, high maintenance cost, and the fact that RTO (Recovery Time Objective) and RPO (Recovery Point Objective) cannot meet expectations The solution is to use sharding middleware or a NewSQL database (like TiDB), and the latter is more cost-effective. TiDB adopts a separate computing and storage architecture, which enables you to scale out With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary

in another city. This solution has the disadvantages of low resource utilization, high maintenance cost, and the fact that RTO (Recovery Time Objective) and RPO (Recovery Point Objective) cannot meet expectations The solution is to use sharding middleware or a NewSQL database (like TiDB), and the latter is more cost-effective. TiDB adopts a separate computing and storage architecture, which enables you to scale out With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves the cost. • Data aggregation and secondary

standalone databases cannot meet the data capacity requirements of rapidly growing applications. TiDB is a cost-effective solution that adopts a separate comput- ing and storage architecture, enabling easy scaling With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary adding �→ flexibility and cost savings. Operations like ADD INDEX �→ and IMPORT INTO will be faster, more resilient, more �→ stable, more flexible, and cost less to run.

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standalone databases cannot meet the data capacity requirements of rapidly growing applications. TiDB is a cost-effective solution that adopts a separate comput- ing and storage architecture, enabling easy scaling With a small amount of extra storage cost, you can handle both online transactional processing and real-time data analysis in the same system, which greatly saves cost. • Data aggregation and secondary TiFlash logger. �→ level �→ Modified Changes the default value from "debug" to "INFO" to reduce the cost of logging. TiDB Light- ning tidb.pd �→ - �→ addr Modified Configures the ad- dresses of the PD