  We collect, scattered through literature, as well as we prove some new
properties of two Markov processes that in many ways resemble Wiener and
Ornstein--Uhlenbeck processes. Although processes considered in this paper were
defined either in non-commutative probability context or through quadratic
harnesses we define them once more as so to say 'continuous time '
generalization of a simple, symmetric, discrete time process satisfying simple
conditions imposed on the form of its first two conditional moments. The finite
dimensional distributions of the first one (say X=(X_{t})_{t\geq0} called
q-Wiener) depends on one parameter q\in(-1,1] and of the second one (say
Y=(Y_{t})_{t\inR} called ({\alpha},q)- Ornstein--Uhlenbeck) on two parameters
({\alpha},q)\in(0,\infty)\times(-1,1]. The first one resembles Wiener process
in the sense that for q=1 it is Wiener process but also that for |q|<1 and
\foralln\geq1: t^{n/2}H_{n}(X_{t}/\surdt|q), where (H_{n})_{n\geq0} are the so
called q-Hermite polynomials, are martingales. It does not have however neither
independent increments not allows continuous sample path modification. The
second one resembles Ornstein--Uhlenbeck process. For q=1 it is a classical OU
process. For |q|<1 it is also stationary with correlation function equal to
exp(-{\alpha}|t-s|) and has many properties resembling those of its classical
version. We think that these process are fascinating objects to study posing
many interesting, open questions.

Probability