arXiv subjects

Jalal Mostafa

Publications and source records attributed to Jalal Mostafa.

4 recordsLinked to original sources

arXiv2026

Six Dimensions of Benchmarking Time-Series Databases

Time-series databases (TSDBs) employ diverse storage architectures optimized for specific workload characteristics, leading to distinct performance profiles and bottlenecks that are often not apparent under conventional benchmarking approaches. System architects designing robust data backends must understand which storage engines are efficient for their particular pipelines and which exhibit the lowest risk of encountering future scalability constraints. This paper presents SciTSv2, a benchmarking framework that evaluates time-series databases across six workload dimensions: connection parallelism, batch ingestion, time-series regularity, multi-variate series, mixed workloads, and system metrics. We exploit SciTSv2 to systematically evaluate 4 TSDBs representing distinct storage engines: InfluxDB (Time-Structured Merge tree), TimescaleDB (based on relational databases), ClickHouse (columnar), and DataLayerTS (specialized in regular time-series). We show that each dimension surfaces architectural behavior that dedicated, single-axis benchmarks obscure, including regularity-dependent trade-offs, contention between concurrent reads and writes, and distinct CPU, I/O, and disk-bandwidth-bound bottlenecks. Paired with fine-grained system metrics, SciTSv2 gives architects a diagnostic tool for tracing performance outcomes back to their underlying architectural causes, supporting storage engine selections grounded in empirical, workload-specific evidence.

cs.DB↗

arXiv2026

The Remote Analog to Digital Conversion DAQ System for the TRISTAN Detector Upgrade

The TRISTAN detector is an upgrade to the KATRIN experiment to enable a differential measurement of the tritium $\beta$-decay spectrum to search for sterile neutrinos with keV masses. This entails performing precision electron spectroscopy with over one thousand silicon drift detector pixels, each responsible for recording incident electron rates of $10^5$ counts per second. A project specific data acquisition (DAQ) system is developed to meet the experimental challenges through a remote analog to digital conversion (RADC) design. In this work, the conceptual design of the RADC DAQ is presented along with the built system for operating the TRISTAN detector upgrade. The system includes flexible signal processing logic and data management that is optimized for the high-rate precision measurement.

physics.ins-det↗

arXiv2025

Sterile-neutrino search based on 259 days of KATRIN data

Neutrinos are the most abundant fundamental matter particles in the Universe and play a crucial role in particle physics and cosmology. Neutrino oscillation, discovered about 25 years ago, reveals that the three known species mix with each other. Anomalous results from reactor and radioactive-source experiments suggest a possible fourth neutrino state, the sterile neutrino, which does not interact via the weak force. The KATRIN experiment, primarily designed to measure the neutrino mass via tritium $\beta$-decay, also searches for sterile neutrinos suggested by these anomalies. A sterile-neutrino signal would appear as a distortion in the $\beta$-decay energy spectrum, characterized by a discontinuity in curvature (kink) related to the sterile-neutrino mass. This signature, which depends only on the shape of the spectrum rather than its absolute normalization, offers a robust, complementary approach to reactor experiments. KATRIN examined the energy spectrum of 36 million tritium $\beta$-decay electrons recorded in 259 measurement days within the last 40 electronvolt below the endpoint. The results exclude a substantial part of the parameter space suggested by the gallium anomaly and challenge the Neutrino-4 claim. Together with other neutrino-disappearance experiments, KATRIN probes sterile-to-active mass splittings from a fraction of an electron-volt squared to several hundred electron-volts squared, excluding light sterile neutrinos with mixing angles above a few percent.

hep-ex↗

arXiv2022

SciTS: A Benchmark for Time-Series Databases in Scientific Experiments and Industrial Internet of Things

Time-series data has an increasingly growing usage in Industrial Internet of Things (IIoT) and large-scale scientific experiments. Managing time-series data needs a storage engine that can keep up with their constantly growing volumes while providing an acceptable query latency. While traditional ACID databases favor consistency over performance, many time-series databases with novel storage engines have been developed to provide better ingestion performance and lower query latency. To understand how the unique design of a time-series database affects its performance, we design SciTS, a highly extensible and parameterizable benchmark for time-series data. The benchmark studies the data ingestion capabilities of time-series databases especially as they grow larger in size. It also studies the latencies of 5 practical queries from the scientific experiments use case. We use SciTS to evaluate the performance of 4 databases of 4 distinct storage engines: ClickHouse, InfluxDB, TimescaleDB, and PostgreSQL.

cs.DB↗