I have two series of tuples
$$ A = \{(x_0,t_{01},t_{02}),(x_1,t_{11},t_{12}),\ldots,(x_n,t_{n1},t_{n2})\} \\ B = \{(y_0,tt_{01},tt_{02}),(y_1,tt_{11},tt_{12}),\ldots,(y_m,tt_{m1},tt_{m2})\} $$
where $m \neq n$ and $t$ and $tt$ are both time observations. $t_{i1}$ and $tt_{j1}$ are both the start of some epoch for arbitrary $i$ and $j$. $t_{i2}$ and $tt_{j2}$ are both the end of some epoch for arbitrary $i$ and $j$. $x_i$ and $y_j$ are the observations of a sensor during those epochs.
In my case $t_{i1}$ and $tt_{j1}$ are not guaranteed to match, nor are $t_{i2}$ and $tt_{j2}$. However, they do represent time epochs that overlap with each other. For instance if $t_{11}$ and $t_{12}$ stretches from 2015-01-02 to 2015-01-03 then $tt_{11}$ and $tt_{12}$ might go from 2015-01-01 to 2015-01-04.
What I'm hoping to do is align the observations $x_i$ and $y_j$ temporally into a single sequence that might look like
$$ C = \{(x_0,y_0,ttt_{01},ttt_{02}),(x_1,y_1,ttt_{11},ttt_{12}),\ldots,(x_p,y_p,ttt_{p1},ttt_{p2})\}, $$
where $p$ is the last observation in the sequence and the time observations $ttt_{k1}$ and $ttt_{k2}$ are the start and end of some epoch non-overlapping with later observations of $ttt_{\mathit{whatever}}$.
I've already looked into some of the papers available on the subject and all of the work I've seen deals with image analysis, audio overlap, kernels, and others that seem a bit off focus than what I'm looking for. Is there more basic theory available that deal specifically with the situation I'm describing here? Or is my case just a specific one covered by some of the papers that I've been reading on?